{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo class usage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Landing point dispersion ellipses](https://raw.githubusercontent.com/RocketPy-Team/RocketPy/master/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.svg)\n",
    "\n",
    "This is an advanced use of RocketPy. This notebook runs a Monte Carlo analysis and predicts probability distributions of the rocket's landing point, apogee and other relevant information. \n",
    "\n",
    "The `MonteCarlo` class simplifies the process of performing Monte Carlo simulations. The idea is to take the already defined classes for a standard flight simulation, and create the so called ``Stochastic`` classes, which are used to run the Monte Carlo analysis.\n",
    "\n",
    "This class offers extensive capabilities, and this example notebook covers as many as possible.\n",
    "For a deeper understanding, we recommend checking the class [documentation](https://docs.rocketpy.org/en/latest/).\n",
    "\n",
    "For a more comprehensive conceptual understanding of Monte Carlo Simulations, refer to RocketPy's main reference: [RocketPy: Six Degree-of-Freedom Rocket Trajectory Simulator](https://doi.org/10.1061/(ASCE)AS.1943-5525.0001331).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We import these lines for debugging purposes, only works on Jupyter Notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "from rocketpy import Environment, Flight, Function, MonteCarlo, Rocket, SolidMotor\n",
    "from rocketpy.stochastic import (\n",
    "    StochasticEnvironment,\n",
    "    StochasticFlight,\n",
    "    StochasticNoseCone,\n",
    "    StochasticParachute,\n",
    "    StochasticRailButtons,\n",
    "    StochasticRocket,\n",
    "    StochasticSolidMotor,\n",
    "    StochasticTail,\n",
    "    StochasticTrapezoidalFins,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using Jupyter Notebooks, it is recommended to run the following line to make matplotlib plots which will be shown later interactive and higher quality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Standard Simulation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first create a standard RocketPy simulation objects (e.g. Environment, SolidMotor, etc.) to then create the Stochastic objects.\n",
    "\n",
    "The standard objects created here are the same as in the [First Simulation Page](https://docs.rocketpy.org/en/latest/user/first_simulation.html) of our documentation, so you can go through that if you want to understand what is done in more detail.\n",
    "\n",
    "The only difference here is that we will use a `Environment` with atmospheric model type `Ensemble`. This allows us to run the Monte Carlo analysis with different ensemble members, which are different atmospheric profiles.\n",
    "\n",
    "We will do it all in one single cell for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Impulse of the Solid Motor: 6026.35 Ns\n"
     ]
    }
   ],
   "source": [
    "# Environment\n",
    "env = Environment(latitude=39.389700, longitude=-8.288964, elevation=113)\n",
    "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n",
    "env.set_date((tomorrow.year, tomorrow.month, tomorrow.day, 12))\n",
    "env.set_atmospheric_model(type=\"Ensemble\", file=\"GEFS\")\n",
    "\n",
    "# Motor\n",
    "motor = SolidMotor(\n",
    "    thrust_source=\"../../../data/motors/cesaroni/Cesaroni_M1670.eng\",\n",
    "    dry_mass=1.815,\n",
    "    dry_inertia=(0.125, 0.125, 0.002),\n",
    "    nozzle_radius=33 / 1000,\n",
    "    grain_number=5,\n",
    "    grain_density=1815,\n",
    "    grain_outer_radius=33 / 1000,\n",
    "    grain_initial_inner_radius=15 / 1000,\n",
    "    grain_initial_height=120 / 1000,\n",
    "    grain_separation=5 / 1000,\n",
    "    grains_center_of_mass_position=0.397,\n",
    "    center_of_dry_mass_position=0.317,\n",
    "    nozzle_position=0,\n",
    "    burn_time=3.9,\n",
    "    throat_radius=11 / 1000,\n",
    "    coordinate_system_orientation=\"nozzle_to_combustion_chamber\",\n",
    ")\n",
    "print(f\"Total Impulse of the Solid Motor: {motor.total_impulse} Ns\")\n",
    "\n",
    "# Rocket\n",
    "rocket = Rocket(\n",
    "    radius=127 / 2000,\n",
    "    mass=14.426,\n",
    "    inertia=(6.321, 6.321, 0.034),\n",
    "    power_off_drag=\"../../../data/rockets/calisto/powerOffDragCurve.csv\",\n",
    "    power_on_drag=\"../../../data/rockets/calisto/powerOnDragCurve.csv\",\n",
    "    center_of_mass_without_motor=0,\n",
    "    coordinate_system_orientation=\"tail_to_nose\",\n",
    ")\n",
    "\n",
    "rail_buttons = rocket.set_rail_buttons(\n",
    "    upper_button_position=0.0818,\n",
    "    lower_button_position=-0.618,\n",
    "    angular_position=45,\n",
    ")\n",
    "\n",
    "rocket.add_motor(motor, position=-1.255)\n",
    "\n",
    "nose_cone = rocket.add_nose(length=0.55829, kind=\"vonKarman\", position=1.278)\n",
    "\n",
    "fin_set = rocket.add_trapezoidal_fins(\n",
    "    n=4,\n",
    "    root_chord=0.120,\n",
    "    tip_chord=0.060,\n",
    "    span=0.110,\n",
    "    position=-1.04956,\n",
    "    cant_angle=0.5,\n",
    "    airfoil=(\"../../../data/airfoils/NACA0012-radians.txt\", \"radians\"),\n",
    ")\n",
    "\n",
    "tail = rocket.add_tail(\n",
    "    top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656\n",
    ")\n",
    "Main = rocket.add_parachute(\n",
    "    \"Main\",\n",
    "    cd_s=10.0,\n",
    "    trigger=800,\n",
    "    sampling_rate=105,\n",
    "    lag=1.5,\n",
    "    noise=(0, 8.3, 0.5),\n",
    ")\n",
    "\n",
    "Drogue = rocket.add_parachute(\n",
    "    \"Drogue\",\n",
    "    cd_s=1.0,\n",
    "    trigger=\"apogee\",\n",
    "    sampling_rate=105,\n",
    "    lag=1.5,\n",
    "    noise=(0, 8.3, 0.5),\n",
    ")\n",
    "\n",
    "# Flight\n",
    "test_flight = Flight(\n",
    "    rocket=rocket,\n",
    "    environment=env,\n",
    "    rail_length=5,\n",
    "    inclination=84,\n",
    "    heading=133,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the trajectory of the Flight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_flight.plots.trajectory_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flight trajectory above represents the nominal trajectory of the rocket, without any uncertainties.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Stochastic Objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each RocketPy object, we will create a ``Stochastic`` counterpart that extends the initial model, allowing us to define the uncertainties of each input parameter.\n",
    "\n",
    "Please refer to the [Working with Stochastic Objects](https://docs.rocketpy.org/en/latest/user/stochastic.html#working-with-stochastic-objects) page on RocketPy`s documentation for a more detailed explanation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Starting with the `Environment` object, we will create a `StochasticEnvironment` to specify its uncertainties.\n",
    "\n",
    "In this first example, we will specify the ensemble member and wind velocities factor.\n",
    "\n",
    "Since the ensemble member is a discrete value, **only list type inputs are permitted**. The list will contain the ensemble numbers to be randomly selected during the Monte Carlo simulation. This means that in each iteration, a different ensemble member will be chosen.\n",
    "\n",
    "<!-- The wind velocities factor are also special inputs. They are used to scale the wind velocities in each axis. The factor inputs can only be tuples or lists, since it has no nominal value to get from the standard. Lets scale the wind by a factor of 1.00000 ± 0.2 -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reporting the attributes of the `StochasticEnvironment` object:\n",
      "\n",
      "Constant Attributes:\n",
      "\tdatum                    SIRGAS2000\n",
      "\televation                113\n",
      "\tgravity                  Function from R1 to R1 : (height (m)) → (gravity (m/s²))\n",
      "\tlatitude                 39.3897\n",
      "\tlongitude                -8.288964\n",
      "\ttimezone                 UTC\n",
      "\n",
      "Stochastic Attributes:\n",
      "\twind_velocity_x_factor   1.00000 ± 0.00000 (normal)\n",
      "\twind_velocity_y_factor   1.00000 ± 0.00000 (normal)\n",
      "\n",
      "Stochastic Attributes with choice of values:\n",
      "\tensemble_member [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]\n"
     ]
    }
   ],
   "source": [
    "stochastic_env = StochasticEnvironment(\n",
    "    environment=env,\n",
    "    ensemble_member=list(range(env.num_ensemble_members)),\n",
    ")\n",
    "\n",
    "stochastic_env.visualize_attributes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE\n",
    "\n",
    "Always check the `.visualize_attributes()` method of each stochastic object to verify the uncertainties were correctly set.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to illustrate the potential of this technique, let's randomly generate 5 instances of the environment using the `create_object` method.\n",
    "\n",
    "For each instance, we will calculate the wind speed at 1km altitude and store the results in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.717859855893523, 4.439778613233867, 2.2538981612637707, 0.8915877554321416, 2.1553975254175404]\n"
     ]
    }
   ],
   "source": [
    "wind_speed_at_1000m = []\n",
    "for i in range(5):\n",
    "    rnd_env = stochastic_env.create_object()\n",
    "    wind_speed_at_1000m.append(rnd_env.wind_velocity_x(1000))\n",
    "\n",
    "print(wind_speed_at_1000m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the wind speed varies between ensemble members.\n",
    "This demonstrates how the Monte Carlo simulation can capture the variability in wind conditions due to different ensemble members.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Motor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a `StochasticSolidMotor` object to define the uncertainties associated with the motor.\n",
    "In this example, we will apply more complex uncertainties to the motor parameters.\n",
    "\n",
    "The `StochasticSolidMotor` also has one special parameter which is the `total_impulse`. It lets us alter the total impulse of the motor while maintaining the thrust curve shape. This is particularly useful for motor uncertainties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reporting the attributes of the `StochasticSolidMotor` object:\n",
      "\n",
      "Constant Attributes:\n",
      "\tburn_out_time                    3.9\n",
      "\tcenter_of_dry_mass_position      0.317\n",
      "\tcoordinate_system_orientation    nozzle_to_combustion_chamber\n",
      "\tdry_I_11                         0.125\n",
      "\tdry_I_12                         0\n",
      "\tdry_I_13                         0\n",
      "\tdry_I_22                         0.125\n",
      "\tdry_I_23                         0\n",
      "\tdry_I_33                         0.002\n",
      "\tdry_mass                         1.815\n",
      "\tgrain_number                     5\n",
      "\tinterpolate                      linear\n",
      "\tthrust_source                    [[0, 0], [0.055, 100.0], [0.092, 1500.0], [0.1, 2000.0], [0.15, 2200.0], [0.2, 1800.0], [0.5, 1950.0], [1.0, 2034.0], [1.5, 2000.0], [2.0, 1900.0], [2.5, 1760.0], [2.9, 1700.0], [3.0, 1650.0], [3.3, 530.0], [3.4, 350.0], [3.9, 0.0]]\n",
      "\n",
      "Stochastic Attributes:\n",
      "\tburn_start_time                  0.00000 ± 0.10000 (binomial)\n",
      "\tgrain_density                    1815.00000 ± 50.00000 (normal)\n",
      "\tgrain_initial_height             0.12000 ± 0.00100 (normal)\n",
      "\tgrain_initial_inner_radius       0.01500 ± 0.00038 (normal)\n",
      "\tgrain_outer_radius               0.03300 ± 0.00038 (normal)\n",
      "\tgrain_separation                 0.00500 ± 0.00100 (normal)\n",
      "\tgrains_center_of_mass_position   0.39700 ± 0.00100 (normal)\n",
      "\tnozzle_position                  0.00000 ± 0.00100 (normal)\n",
      "\tnozzle_radius                    0.03300 ± 0.00050 (normal)\n",
      "\tthroat_radius                    0.01100 ± 0.00050 (normal)\n",
      "\ttotal_impulse                    6500.00000 ± 1000.00000 (normal)\n"
     ]
    }
   ],
   "source": [
    "stochastic_motor = StochasticSolidMotor(\n",
    "    solid_motor=motor,\n",
    "    burn_start_time=(0, 0.1, \"binomial\"),\n",
    "    grains_center_of_mass_position=0.001,\n",
    "    grain_density=50,\n",
    "    grain_separation=1 / 1000,\n",
    "    grain_initial_height=1 / 1000,\n",
    "    grain_initial_inner_radius=0.375 / 1000,\n",
    "    grain_outer_radius=0.375 / 1000,\n",
    "    total_impulse=(6500, 1000),\n",
    "    throat_radius=0.5 / 1000,\n",
    "    nozzle_radius=0.5 / 1000,\n",
    "    nozzle_position=0.001,\n",
    ")\n",
    "stochastic_motor.visualize_attributes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE\n",
    "\n",
    "Pay special attention to how different input types are interpreted in the `StochasticSolidMotor` object by checking the printed object:\n",
    "\n",
    "\n",
    "<!-- - ``thrust_source`` was given as a list of 3 items, and is saved as is. This means that the simulation will randomly chose one item of that list, as desired -->\n",
    "\n",
    "- ``burn_start_time`` was given as a tuple of 3 items, specifying the nominal value, the standard deviation and the distribution type\n",
    "\n",
    "- ``total_impulse`` was given as a tuple of 2 numbers, so the distribution type was set to the default: `normal`\n",
    "\n",
    "- All other values set for the other parameters in the constructor are simple values, which means they are interpreted as standard deviation and the nominal value is taken from the ``motor``\n",
    "\n",
    "- The remaining parameters that are printed are just the nominal values from the ``motor``. In the ``Stochastic`` object they are saved as a list of one item\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we can illustrate the power of stochastic modeling by generating multiple instances of the `SolidMotor` class using the `StochasticSolidMotor` object.\n",
    "For each instance, we will calculate the total impulse and store the results in a list. This will show how the uncertainties in the motor parameters affect the total impulse over multiple iterations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5355.9143244739935, 6320.4738883261325, 7394.177114563075, 5358.212881446587, 6619.455732940446]\n"
     ]
    }
   ],
   "source": [
    "total_impulse = []\n",
    "for i in range(5):\n",
    "    rnd_motor = stochastic_motor.create_object()\n",
    "    total_impulse.append(rnd_motor.total_impulse)\n",
    "\n",
    "print(total_impulse)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rocket\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a `StochasticRocket` object to define the uncertainties associated with the rocket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reporting the attributes of the `StochasticRocket` object:\n",
      "\n",
      "Constant Attributes:\n",
      "\tI_12_without_motor              0\n",
      "\tI_13_without_motor              0\n",
      "\tI_23_without_motor              0\n",
      "\tcoordinate_system_orientation   tail_to_nose\n",
      "\tpower_off_drag                  Function from R1 to R1 : (Mach Number) → (Drag Coefficient with Power Off)\n",
      "\tpower_on_drag                   Function from R1 to R1 : (Mach Number) → (Drag Coefficient with Power On)\n",
      "\n",
      "Stochastic Attributes:\n",
      "\tI_11_without_motor              6.32100 ± 0.00000 (normal)\n",
      "\tI_22_without_motor              6.32100 ± 0.01000 (normal)\n",
      "\tI_33_without_motor              0.03400 ± 0.01000 (normal)\n",
      "\tcenter_of_mass_without_motor    0.00000 ± 0.00000 (normal)\n",
      "\tmass                            15.42600 ± 0.50000 (normal)\n",
      "\tpower_off_drag_factor           1.00000 ± 0.00000 (normal)\n",
      "\tpower_on_drag_factor            1.00000 ± 0.00000 (normal)\n",
      "\tradius                          0.06350 ± 0.00001 (normal)\n"
     ]
    }
   ],
   "source": [
    "stochastic_rocket = StochasticRocket(\n",
    "    rocket=rocket,\n",
    "    radius=0.0127 / 2000,\n",
    "    mass=(15.426, 0.5, \"normal\"),\n",
    "    inertia_11=(6.321, 0),\n",
    "    inertia_22=0.01,\n",
    "    inertia_33=0.01,\n",
    "    center_of_mass_without_motor=0,\n",
    ")\n",
    "stochastic_rocket.visualize_attributes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `StochasticRocket` still needs to have its aerodynamic surfaces and parachutes added. \n",
    "\n",
    "We can also create stochastic models for each aerodynamic surface, although this is not mandatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stochastic_nose_cone = StochasticNoseCone(\n",
    "    nosecone=nose_cone,\n",
    "    length=0.001,\n",
    ")\n",
    "\n",
    "stochastic_fin_set = StochasticTrapezoidalFins(\n",
    "    trapezoidal_fins=fin_set,\n",
    "    root_chord=0.0005,\n",
    "    tip_chord=0.0005,\n",
    "    span=0.0005,\n",
    ")\n",
    "\n",
    "stochastic_tail = StochasticTail(\n",
    "    tail=tail,\n",
    "    top_radius=0.001,\n",
    "    bottom_radius=0.001,\n",
    "    length=0.001,\n",
    ")\n",
    "\n",
    "stochastic_rail_buttons = StochasticRailButtons(\n",
    "    rail_buttons=rail_buttons, buttons_distance=0.001\n",
    ")\n",
    "\n",
    "stochastic_main = StochasticParachute(\n",
    "    parachute=Main,\n",
    "    cd_s=0.1,\n",
    "    lag=0.1,\n",
    ")\n",
    "\n",
    "stochastic_drogue = StochasticParachute(\n",
    "    parachute=Drogue,\n",
    "    cd_s=0.07,\n",
    "    lag=0.2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we must add them to our stochastic rocket, much like we do in the normal Rocket.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "stochastic_rocket.add_motor(stochastic_motor, position=0.001)\n",
    "stochastic_rocket.add_nose(stochastic_nose_cone, position=(1.134, 0.001))\n",
    "stochastic_rocket.add_trapezoidal_fins(stochastic_fin_set, position=(0.001, \"normal\"))\n",
    "stochastic_rocket.add_tail(stochastic_tail)\n",
    "stochastic_rocket.set_rail_buttons(\n",
    "    stochastic_rail_buttons, lower_button_position=(0.001, \"normal\")\n",
    ")\n",
    "stochastic_rocket.add_parachute(stochastic_main)\n",
    "stochastic_rocket.add_parachute(stochastic_drogue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE\n",
    "\n",
    "The `position` arguments behave just like the other ``Stochastic`` classes parameters\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets check how the `StochasticRocket` handled all these additions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reporting the attributes of the `StochasticRocket` object:\n",
      "\n",
      "Constant Attributes:\n",
      "\tI_12_without_motor              0\n",
      "\tI_13_without_motor              0\n",
      "\tI_23_without_motor              0\n",
      "\tcoordinate_system_orientation   tail_to_nose\n",
      "\tpower_off_drag                  Function from R1 to R1 : (Mach Number) → (Drag Coefficient with Power Off)\n",
      "\tpower_on_drag                   Function from R1 to R1 : (Mach Number) → (Drag Coefficient with Power On)\n",
      "\n",
      "Stochastic Attributes:\n",
      "\tI_11_without_motor              6.32100 ± 0.00000 (normal)\n",
      "\tI_22_without_motor              6.32100 ± 0.01000 (normal)\n",
      "\tI_33_without_motor              0.03400 ± 0.01000 (normal)\n",
      "\tcenter_of_mass_without_motor    0.00000 ± 0.00000 (normal)\n",
      "\tmass                            15.42600 ± 0.50000 (normal)\n",
      "\tpower_off_drag_factor           1.00000 ± 0.00000 (normal)\n",
      "\tpower_on_drag_factor            1.00000 ± 0.00000 (normal)\n",
      "\tradius                          0.06350 ± 0.00001 (normal)\n"
     ]
    }
   ],
   "source": [
    "stochastic_rocket.visualize_attributes()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Flight\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After defining the `Flight`, we can create the corresponding `Stochastic` object to define the uncertainties of the input parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reporting the attributes of the `StochasticFlight` object:\n",
      "\n",
      "Constant Attributes:\n",
      "\trail_length           5\n",
      "\n",
      "Stochastic Attributes:\n",
      "\theading               53.00000 ± 2.00000 (normal)\n",
      "\tinclination           84.70000 ± 1.00000 (normal)\n"
     ]
    }
   ],
   "source": [
    "stochastic_flight = StochasticFlight(\n",
    "    flight=test_flight,\n",
    "    inclination=(84.7, 1),  # mean= 84.7, std=1\n",
    "    heading=(53, 2),  # mean= 53, std=2\n",
    ")\n",
    "stochastic_flight.visualize_attributes()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Starting the Monte Carlo Simulations\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's invoke the `MonteCarlo` class, we are going to need a filename to initialize it.\n",
    "The filename will be used either to save the results of the simulations or to load them\n",
    "from a previous ran simulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Mateus\\GitHub\\RocketPy\\rocketpy\\simulation\\monte_carlo.py:137: UserWarning: This class is still under testing and some attributes may be changed in next versions\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following input file was imported: monte_carlo_analysis_outputs\\monte_carlo_class_example.inputs.txt\n",
      "A total of 20 simulations results were loaded from the following output file: monte_carlo_analysis_outputs\\monte_carlo_class_example.outputs.txt\n",
      "\n",
      "The following error file was imported: monte_carlo_analysis_outputs\\monte_carlo_class_example.errors.txt                                         \n"
     ]
    }
   ],
   "source": [
    "test_dispersion = MonteCarlo(\n",
    "    filename=\"monte_carlo_analysis_outputs/monte_carlo_class_example\",\n",
    "    environment=stochastic_env,\n",
    "    rocket=stochastic_rocket,\n",
    "    flight=stochastic_flight,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's simulate our flights. \n",
    "We can run the simulations using the method `MonteCarlo.simulate()`.\n",
    "\n",
    "Set `append=False` to overwrite the previous results, or `append=True` to add the new results to the previous ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Monte Carlo analysis                                                                           \n",
      "Running Monte Carlo simulation with 4 workers.\n",
      "Results saved to monte_carlo_analysis_outputs\\monte_carlo_class_example.outputs.txt\n"
     ]
    }
   ],
   "source": [
    "test_dispersion.simulate(\n",
    "    number_of_simulations=10,\n",
    "    append=False,\n",
    "    include_function_data=False,\n",
    "    parallel=True,\n",
    "    n_workers=4,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we finally have the results of our Monte Carlo simulations loaded!\n",
    "Let's play with them.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can print numerical information regarding the results of the simulations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You only need to import results if you did not run the simulations\n",
    "# test_dispersion.import_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dispersion.num_of_loaded_sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monte Carlo Simulation by RocketPy\n",
      "Data Source:  monte_carlo_analysis_outputs/monte_carlo_class_example\n",
      "Number of simulations:  1000\n",
      "Results: \n",
      "\n",
      "                Parameter            Mean       Std. Dev.\n",
      "------------------------------------------------------------\n",
      "                   apogee        3221.616         594.740\n",
      "     frontal_surface_wind          -3.839           0.481\n",
      "                 apogee_y         876.923         123.791\n",
      "     lateral_surface_wind          -5.259           0.621\n",
      "                  t_final         299.027          34.327\n",
      "     out_of_rail_velocity          25.690           2.194\n",
      " initial_stability_margin           2.596           0.085\n",
      "         out_of_rail_time           0.361           0.027\n",
      "                 y_impact       -1839.493         486.133\n",
      "                 apogee_x         446.565         124.777\n",
      "          impact_velocity          -5.259           0.378\n",
      "          max_mach_number           0.859           0.131\n",
      "                 x_impact         312.319         195.323\n",
      "out_of_rail_stability_margin           2.669           0.084\n",
      "              apogee_time          24.782           2.015\n"
     ]
    }
   ],
   "source": [
    "test_dispersion.prints.all()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, we can plot the results of the simulations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dispersion.plots.ellipses(xlim=(-200, 3500), ylim=(-200, 3500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ec/Nl28WLF1flypVtLqcuVqyYJNlc+ipJHTp0UEZGht5//32b9nfffVcWi0Xt27eXJOu5/w8++MCm39y5c/Nc541/od78L9K7deffDh06ZPt677zzjiTlemWZo+v48ccftXv3bmtbSkqKPvzwQ5UvXz7X0y05KVasWLanpbLb53v27LF57dt18++dm5ubdY7Zjd+9GyHi31cnpaSkWEd+7oS7u7ssFovNKNGJEye0evVqm35dunSRm5ubpk6dmmX08MZ+CQsLk4+Pj956661sr4A8f/68XbWFhoYqNTVVs2fPVvPmza1BMzQ0VEuXLtXp06fzNF8nL9zd3RUWFqbVq1fr1KlT1vbDhw9r48aNDnkN5D+M7OCeU716dbVs2VINGjSQv7+/9u7dqxUrVmjYsGHWPg0aNJAkDR8+XGFhYXJ3d1f37t3VqVMntWrVSq+99ppOnDihOnXqaNOmTVqzZo1Gjhxp/WPVoEEDdevWTbNnz9bFixetl57/8ccfkvJ2asjHx0ctWrTQjBkzlJaWpvvvv1+bNm1SXFycE/ZKVnXq1FGfPn304YcfKiEhQY888oh+/PFHLVmyRF26dFGrVq3uSh3jxo3TZ599pvbt22v48OHy9/fXkiVLFBcXp6+++sruu2NL/xyfL774QuHh4XrooYdUvHhxderUSY8//rhWrlypJ598Uh07dlRcXJwWLFig6tWrW+/vcrteeOEFXbp0Sa1bt1aZMmV08uRJzZ07V3Xr1rWOnD322GMqW7asBgwYoNGjR8vd3V2LFi1SQECAzR/m29GxY0e98847ateunXr27Klz585p3rx5qly5ss2ctcqVK+u1117T66+/rtDQUHXt2lWenp766aefFBwcrMjISPn4+Gj+/Pl6/vnnVb9+fXXv3t1a47p169SsWbMs/yDITZMmTVSoUCEdOXJEgwYNsra3aNFC8+fPlySHhR3pn0vrN2zYoNDQUA0ZMkTp6emaO3euatSokef5eyhgXHchGGC/G5fg/vTTT9muz+6S25svPX/jjTeMRo0aGX5+fkaRIkWMatWqGW+++aZx/fp1a5/09HTj5ZdfNgICAgyLxWJzOeqVK1eMUaNGGcHBwUbhwoWNKlWqGDNnzrRelntDSkqKMXToUMPf398oXry40aVLF+PIkSOGJJtLwXO6FNswDOOvv/4ynnzyScPPz8/w9fU1nn76aeP06dM5Xr5+8zZyuiQ8p0uTb5aWlmZMmTLFqFChglG4cGEjJCTEiIiIMK5du5an18lObpee53RLgNjYWOOpp54y/Pz8DC8vL6NRo0bG2rVrbfrcuPR8+fLlNu1xcXGGJCMqKsralpycbPTs2dPw8/MzJFkvQ8/MzDTeeusto1y5coanp6dRr149Y+3atUafPn2yXKp+8zG4lRUrVhiPPfaYERgYaHh4eBhly5Y1Bg8ebJw5c8am3759+4zGjRtb+7zzzjs5Xnqe0/7K6fguXLjQqFKliuHp6WlUq1bNiIqKyvFy90WLFhn16tUzPD09jfvuu8945JFHjOjoaJs+27ZtM8LCwgxfX1/Dy8vLqFSpktG3b19j7969ed4vNzz00EOGJGPPnj3Wtr/++suQZISEhGTpn9Ol50OHDs3S9+b/BxiGYezYscNo0KCB4eHhYVSsWNFYsGBBjvsCBZ/FMJi1BdwtMTExqlevnj755BP16tXL1eUAwD2BOTuAk2T3vU6zZ8+Wm5vbLe9cDABwHObsAE4yY8YM7du3T61atVKhQoWslxsPGjRIISEhri4PDpKRkXHLCbnFixe/5dccmM3169d16dKlXPv4+vo65HYDwK1wGgtwkujoaE2ZMkW//fabkpOTVbZsWT3//PN67bXX7LoPCfK3EydO3PLmfpMmTbL58td7wfbt2285iT0qKsrmS3oBZyHsAMAduHbtmr799ttc+1SsWFEVK1a8SxXlD5cvX9a+ffty7VOjRg2VLl36LlWEexlhBwAAmBoTlAEAgKkxcUD/fM/L6dOn5e3tfVe/BwgAANw+wzB05coVBQcH53qDUcKOpNOnT3N1DAAABdSff/6pMmXK5LiesCNZv9Dxzz//lI+Pj4urAQAAeZGUlKSQkBCbL2bODmFH//97inx8fAg7AAAUMLeagsIEZQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqFXF0AcK8qP26dq0u4LSemdXR1CQBgF0Z2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqbk07OzcuVOdOnVScHCwLBaLVq9ebbPeYrFku8ycOdPap3z58lnWT5s27S6/EwAAkF+5NOykpKSoTp06mjdvXrbrz5w5Y7MsWrRIFotF3bp1s+k3depUm34vv/zy3SgfAAAUAIVc+eLt27dX+/btc1wfFBRk83jNmjVq1aqVKlasaNPu7e2dpS8AAIBUgObsnD17VuvWrdOAAQOyrJs2bZpKlCihevXqaebMmUpPT891W6mpqUpKSrJZAACAObl0ZMceS5Yskbe3t7p27WrTPnz4cNWvX1/+/v76/vvvFRERoTNnzuidd97JcVuRkZGaMmWKs0sGAAD5QIEJO4sWLVKvXr3k5eVl0x4eHm79uXbt2vLw8NDgwYMVGRkpT0/PbLcVERFh87ykpCSFhIQ4p3AAAOBSBSLs7Nq1S0eOHNEXX3xxy76NGzdWenq6Tpw4oapVq2bbx9PTM8cgBAAAzKVAzNlZuHChGjRooDp16tyyb0xMjNzc3BQYGHgXKgMAAPmdS0d2kpOTdezYMevjuLg4xcTEyN/fX2XLlpX0zymm5cuXa9asWVmev3v3bu3Zs0etWrWSt7e3du/erVGjRum5557Tfffdd9feBwAAyL9cGnb27t2rVq1aWR/fmEfTp08fLV68WJL0+eefyzAM9ejRI8vzPT099fnnn2vy5MlKTU1VhQoVNGrUKJv5OAAA4N5mMQzDcHURrpaUlCRfX18lJibKx8fH1eXgHlF+3DpXl3DPODGto6tLAOAEef37XSDm7AAAANwuwg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1Qq4uAHCE8uPWuboEAEA+xcgOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNZeGnZ07d6pTp04KDg6WxWLR6tWrbdb37dtXFovFZmnXrp1Nn0uXLqlXr17y8fGRn5+fBgwYoOTk5Lv4LgAAQH7m0rCTkpKiOnXqaN68eTn2adeunc6cOWNdPvvsM5v1vXr10qFDhxQdHa21a9dq586dGjRokLNLBwAABUQhV754+/bt1b59+1z7eHp6KigoKNt1hw8f1oYNG/TTTz+pYcOGkqS5c+eqQ4cOevvttxUcHJzt81JTU5Wammp9nJSUdJvvAAAA5Hf5fs7O9u3bFRgYqKpVq+qll17SxYsXret2794tPz8/a9CRpLZt28rNzU179uzJcZuRkZHy9fW1LiEhIU59DwAAwHXyddhp166dPv74Y23ZskXTp0/Xjh071L59e2VkZEiS4uPjFRgYaPOcQoUKyd/fX/Hx8TluNyIiQomJidblzz//dOr7AAAAruPS01i30r17d+vPtWrVUu3atVWpUiVt375dbdq0ue3tenp6ytPT0xElAgCAfC5fj+zcrGLFiipZsqSOHTsmSQoKCtK5c+ds+qSnp+vSpUs5zvMBAAD3lgIVdv766y9dvHhRpUuXliQ1adJECQkJ2rdvn7XP1q1blZmZqcaNG7uqTAAAkI+49DRWcnKydZRGkuLi4hQTEyN/f3/5+/trypQp6tatm4KCghQbG6sxY8aocuXKCgsLkyQ9+OCDateunQYOHKgFCxYoLS1Nw4YNU/fu3XO8EgsAANxbXDqys3fvXtWrV0/16tWTJIWHh6tevXqaOHGi3N3ddfDgQXXu3FkPPPCABgwYoAYNGmjXrl02820+/fRTVatWTW3atFGHDh3UvHlzffjhh656SwAAIJ9x6chOy5YtZRhGjus3btx4y234+/tr2bJljiwLAACYSIGaswMAAGAvwg4AADA1wg4AADA1wg4AADA1wg4AADA1u8PO/v379csvv1gfr1mzRl26dNF//vMfXb9+3aHFAQAA3Cm7w87gwYP1xx9/SJKOHz+u7t27q2jRolq+fLnGjBnj8AIBAADuhN1h548//lDdunUlScuXL1eLFi20bNkyLV68WF999ZWj6wMAALgjdocdwzCUmZkpSdq8ebM6dOggSQoJCdGFCxccWx0AAMAdsjvsNGzYUG+88YaWLl2qHTt2qGPHjpL++V6rUqVKObxAAACAO2F32Jk9e7b279+vYcOG6bXXXlPlypUlSStWrFDTpk0dXiAAAMCdsPu7sWrXrm1zNdYNM2fOlLu7u0OKAgAAcJTbus9OQkKCPvroI0VEROjSpUuSpN9++03nzp1zaHEAAAB3yu6RnYMHD6pNmzby8/PTiRMnNHDgQPn7+2vlypU6deqUPv74Y2fUCQAAcFvsHtkJDw9Xv379dPToUXl5eVnbO3TooJ07dzq0OAAAgDtld9j56aefNHjw4Czt999/v+Lj4x1SFAAAgKPYHXY8PT2VlJSUpf2PP/5QQECAQ4oCAABwFLvDTufOnTV16lSlpaVJkiwWi06dOqWxY8eqW7duDi8QAADgTtgddmbNmqXk5GQFBgbq77//1iOPPKLKlSvL29tbb775pjNqBAAAuG12X43l6+ur6Ohofffdd/r555+VnJys+vXrq23bts6oDwAA4I7YHXZuaNasmZo1a+bIWgAAABzO7tNYw4cP15w5c7K0v//++xo5cqQjagIAAHAYu8POV199le2ITtOmTbVixQqHFAUAAOAodoedixcvytfXN0u7j4+PLly44JCiAAAAHMXusFO5cmVt2LAhS/v69etVsWJFhxQFAADgKHZPUA4PD9ewYcN0/vx5tW7dWpK0ZcsWzZo1S7Nnz3Z0fQAAAHfE7rDTv39/paam6s0339Trr78uSSpfvrzmz5+v3r17O7xAAACAO3Fbl56/9NJLeumll3T+/HkVKVJExYsXd3RdAAAADnHb99mRxHdhAQCAfM/uCcpnz57V888/r+DgYBUqVEju7u42CwAAQH5i98hO3759derUKU2YMEGlS5eWxWJxRl0AAAAOYXfY+fbbb7Vr1y7VrVvXCeUAAAA4lt2nsUJCQmQYhjNqAQAAcDi7w87s2bM1btw4nThxwgnlAAAAOJbdp7GeffZZXb16VZUqVVLRokVVuHBhm/WXLl1yWHEAAAB3yu6w48i7JO/cuVMzZ87Uvn37dObMGa1atUpdunSRJKWlpWn8+PH65ptvdPz4cfn6+qpt27aaNm2agoODrdsoX768Tp48abPdyMhIjRs3zmF1AgCAgsvusNOnTx+HvXhKSorq1Kmj/v37q2vXrjbrrl69qv3792vChAmqU6eOLl++rBEjRqhz587au3evTd+pU6dq4MCB1sfe3t4OqxEAABRst3VTwdjYWEVFRSk2NlbvvfeeAgMDtX79epUtW1Y1atTI83bat2+v9u3bZ7vO19dX0dHRNm3vv/++GjVqpFOnTqls2bLWdm9vbwUFBd3OWwEAACZn9wTlHTt2qFatWtqzZ49Wrlyp5ORkSdLPP/+sSZMmObzAf0tMTJTFYpGfn59N+7Rp01SiRAnVq1dPM2fOVHp6eq7bSU1NVVJSks0CAADMye6wM27cOL3xxhuKjo6Wh4eHtb1169b64YcfHFrcv127dk1jx45Vjx495OPjY20fPny4Pv/8c23btk2DBw/WW2+9pTFjxuS6rcjISPn6+lqXkJAQp9UNAABcy+7TWL/88ouWLVuWpT0wMFAXLlxwSFE3S0tL0zPPPCPDMDR//nybdeHh4dafa9euLQ8PDw0ePFiRkZHy9PTMdnsRERE2z0tKSiLwAABgUnaP7Pj5+enMmTNZ2g8cOKD777/fIUX9242gc/LkSUVHR9uM6mSncePGSk9Pz/U+QJ6envLx8bFZAACAOdkddrp3766xY8cqPj5eFotFmZmZ+u677/Tqq6+qd+/eDi3uRtA5evSoNm/erBIlStzyOTExMXJzc1NgYKBDawEAAAWT3aex3nrrLQ0dOlQhISHKyMhQ9erVlZGRoZ49e2r8+PF2bSs5OVnHjh2zPo6Li1NMTIz8/f1VunRpPfXUU9q/f7/Wrl2rjIwMxcfHS5L8/f3l4eGh3bt3a8+ePWrVqpW8vb21e/dujRo1Ss8995zuu+8+e98aAAAwIYthxxddGYahP//8UwEBAbpw4YJ++eUXJScnq169eqpSpYrdL759+3a1atUqS3ufPn00efJkVahQIdvnbdu2TS1bttT+/fs1ZMgQ/f7770pNTVWFChX0/PPPKzw8PMf5OtlJSkqSr6+vEhMTOaVVQJUft87VJSAfOzGto6tLAOAEef37bdfIjmEYqly5sg4dOqQqVarc8aTeli1b5vqlorfKYfXr13fqFWAAAKDgs2vOjpubm6pUqaKLFy86qx4AAACHsnuC8rRp0zR69Gj9+uuvzqgHAADAoeyeoNy7d29dvXpVderUkYeHh4oUKWKznm89BwAA+YlLv/UcAADA2ewKO2lpadqxY4cmTJiQ45VSAAAA+Yldc3YKFy6sr776ylm1AAAAOJzdE5S7dOmi1atXO6EUAAAAx7N7zk6VKlU0depUfffdd2rQoIGKFStms3748OEOKw4AAOBO2R12Fi5cKD8/P+3bt0/79u2zWWexWAg7AAAgX7E77MTFxTmjDgAAAKewe84OAABAQWL3yE7//v1zXb9o0aLbLgYAAMDR7A47ly9ftnmclpamX3/9VQkJCWrdurXDCgMAAHAEu8POqlWrsrRlZmbqpZdeUqVKlRxSFAAAgKM4ZM6Om5ubwsPD9e677zpicwAAAA7jsAnKsbGxSk9Pd9TmAAAAHMLu01jh4eE2jw3D0JkzZ7Ru3Tr16dPHYYUBAAA4gt1h58CBAzaP3dzcFBAQoFmzZt3ySi0AAIC7ze6ws23bNmfUAQAA4BR2z9mJi4vT0aNHs7QfPXpUJ06ccERNAAAADmN32Onbt6++//77LO179uxR3759HVETAACAw9gddg4cOKBmzZplaX/44YcVExPjiJoAAAAcxu6wY7FYdOXKlSztiYmJysjIcEhRAAAAjmJ32GnRooUiIyNtgk1GRoYiIyPVvHlzhxYHAABwp+y+Gmv69Olq0aKFqlatqtDQUEnSrl27lJSUpK1btzq8QAAAgDth98hO9erVdfDgQT3zzDM6d+6crly5ot69e+v3339XzZo1nVEjAADAbbN7ZEeSgoOD9dZbbzm6FgAAAIeze2QnKipKy5cvz9K+fPlyLVmyxCFFAQAAOIrdYScyMlIlS5bM0h4YGMhoDwAAyHfsDjunTp1ShQoVsrSXK1dOp06dckhRAAAAjmJ32AkMDNTBgweztP/8888qUaKEQ4oCAABwFLvDTo8ePTR8+HBt27ZNGRkZysjI0NatWzVixAh1797dGTUCAADcNruvxnr99dd14sQJtWnTRoUK/fP0zMxM9e7dmzk7AAAg37E77Hh4eOiLL77Q66+/rp9//llFihRRrVq1VK5cOWfUBwAoIMqPW+fqEux2YlpHV5eAu+C27rMjSf7+/mrVqlW2V2YBAADkF3bN2UlISNDQoUNVsmRJlSpVSqVKlVLJkiU1bNgwJSQk2P3iO3fuVKdOnRQcHCyLxaLVq1fbrDcMQxMnTlTp0qVVpEgRtW3bVkePHrXpc+nSJfXq1Us+Pj7y8/PTgAEDlJycbHctAADAnPIcdi5duqTGjRtryZIl6tatm2bNmqVZs2apa9euWrx4sZo0aaLLly/b9eIpKSmqU6eO5s2bl+36GTNmaM6cOVqwYIH27NmjYsWKKSwsTNeuXbP26dWrlw4dOqTo6GitXbtWO3fu1KBBg+yqAwAAmFeeT2NNnTpVHh4eio2NValSpbKse+yxxzR16lS9++67eX7x9u3bq3379tmuMwxDs2fP1vjx4/XEE09Ikj7++GOVKlVKq1evVvfu3XX48GFt2LBBP/30kxo2bChJmjt3rjp06KC3335bwcHBea4FAACYU55HdlavXq233347S9CRpKCgIM2YMUOrVq1yWGFxcXGKj49X27ZtrW2+vr5q3Lixdu/eLUnavXu3/Pz8rEFHktq2bSs3Nzft2bMnx22npqYqKSnJZgEAAOaU57Bz5swZ1ahRI8f1NWvWVHx8vEOKkmTd1s3hqlSpUtZ18fHxCgwMtFlfqFAh+fv751pLZGSkfH19rUtISIjD6gYAAPlLnsNOyZIldeLEiRzXx8XFyd/f3xE1OV1ERIQSExOty59//unqkgAAgJPkOeyEhYXptdde0/Xr17OsS01N1YQJE9SuXTuHFRYUFCRJOnv2rE372bNnreuCgoJ07tw5m/Xp6em6dOmStU92PD095ePjY7MAAABzsmuCcsOGDVWlShUNHTpU1apVk2EYOnz4sD744AOlpqZq6dKlDiusQoUKCgoK0pYtW1S3bl1JUlJSkvbs2aOXXnpJktSkSRMlJCRo3759atCggSRp69atyszMVOPGjR1WCwAAKLjyHHbKlCmj3bt3a8iQIYqIiJBhGJIki8WiRx99VO+//77dc1+Sk5N17Ngx6+O4uDjFxMTI399fZcuW1ciRI/XGG2+oSpUqqlChgiZMmKDg4GB16dJFkvTggw+qXbt2GjhwoBYsWKC0tDQNGzZM3bt350osAAAgyc47KFeoUEHr16/X5cuXrTf3q1y58m3P1dm7d69atWplfRweHi5J6tOnjxYvXqwxY8YoJSVFgwYNUkJCgpo3b64NGzbIy8vL+pxPP/1Uw4YNU5s2beTm5qZu3bppzpw5t1UPAAAwH4txY4jmHpaUlCRfX18lJiYyf6eAKojfyYO7h+8/ujsK4ueQ342CLa9/v2/7u7EAoKDgjzBwb7Pru7EAAAAKGsIOAAAwtTyFnfr161u/5HPq1Km6evWqU4sCAABwlDyFncOHDyslJUWSNGXKFCUnJzu1KAAAAEfJ0wTlunXrql+/fmrevLkMw9Dbb7+t4sWLZ9t34sSJDi0QAADgTuQp7CxevFiTJk3S2rVrZbFYtH79ehUqlPWpFouFsAMAAPKVPIWdqlWr6vPPP5ckubm5acuWLVm+bRwAACA/svs+O5mZmc6oAwAAwClu66aCsbGxmj17tg4fPixJql69ukaMGKFKlSo5tDgAAIA7Zfd9djZu3Kjq1avrxx9/VO3atVW7dm3t2bNHNWrUUHR0tDNqBAAAuG12j+yMGzdOo0aN0rRp07K0jx07Vo8++qjDigMAALhTdo/sHD58WAMGDMjS3r9/f/32228OKQoAAMBR7B7ZCQgIUExMjKpUqWLTHhMTwxVaAOAgBfHLS4H8yu6wM3DgQA0aNEjHjx9X06ZNJUnfffedpk+frvDwcIcXCAAAcCfsDjsTJkyQt7e3Zs2apYiICElScHCwJk+erOHDhzu8QAAAgDthd9ixWCwaNWqURo0apStXrkiSvL29HV4YAACAI9zWfXZuIOQAAID8zu6rsQAAAAqSOxrZAQCgICuIV72dmNbR1SUUOIzsAAAAU7Mr7KSlpalNmzY6evSos+oBAABwKLvCTuHChXXw4EFn1QIAAOBwdp/Geu6557Rw4UJn1AIAAOBwdk9QTk9P16JFi7R582Y1aNBAxYoVs1n/zjvvOKw4AACAO2V32Pn1119Vv359SdIff/xhs85isTimKgAAAAexO+xs27bNGXUAAAA4xW1fen7s2DFt3LhRf//9tyTJMAyHFQUAAOAodoedixcvqk2bNnrggQfUoUMHnTlzRpI0YMAAvfLKKw4vEAAA4E7YHXZGjRqlwoUL69SpUypatKi1/dlnn9WGDRscWhwAAMCdsnvOzqZNm7Rx40aVKVPGpr1KlSo6efKkwwoDAABwBLtHdlJSUmxGdG64dOmSPD09HVIUAACAo9gddkJDQ/Xxxx9bH1ssFmVmZmrGjBlq1aqVQ4sDAAC4U3afxpoxY4batGmjvXv36vr16xozZowOHTqkS5cu6bvvvnNGjQAAALfN7pGdmjVr6o8//lDz5s31xBNPKCUlRV27dtWBAwdUqVIlZ9QIAABw2+we2ZEkX19fvfbaa46uJVvly5fPduLzkCFDNG/ePLVs2VI7duywWTd48GAtWLDgrtQHAADyt9sKO5cvX9bChQt1+PBhSVL16tXVr18/+fv7O7Q4Sfrpp5+UkZFhffzrr7/q0Ucf1dNPP21tGzhwoKZOnWp9nN0EagAAcG+y+zTWzp07Vb58ec2ZM0eXL1/W5cuXNWfOHFWoUEE7d+50eIEBAQEKCgqyLmvXrlWlSpX0yCOPWPsULVrUpo+Pj4/D6wAAAAWT3WFn6NChevbZZxUXF6eVK1dq5cqVOn78uLp3766hQ4c6o0ar69ev65NPPlH//v1tvnT0008/VcmSJVWzZk1FRETo6tWruW4nNTVVSUlJNgsAADAnu09jHTt2TCtWrJC7u7u1zd3dXeHh4TaXpDvD6tWrlZCQoL59+1rbevbsqXLlyik4OFgHDx7U2LFjdeTIEa1cuTLH7URGRmrKlClOrRUAAOQPdoed+vXr6/Dhw6patapN++HDh1WnTh2HFZadhQsXqn379goODra2DRo0yPpzrVq1VLp0abVp00axsbE5Xh0WERGh8PBw6+OkpCSFhIQ4r3AAAOAyeQo7Bw8etP48fPhwjRgxQseOHdPDDz8sSfrhhx80b948TZs2zTlVSjp58qQ2b96c64iNJDVu3FjSPyNQOYUdT09P7vYMAMA9Ik9hp27durJYLDIMw9o2ZsyYLP169uypZ5991nHV/UtUVJQCAwPVsWPHXPvFxMRIkkqXLu2UOgAAQMGSp7ATFxfn7DpylZmZqaioKPXp00eFCv3/kmNjY7Vs2TJ16NBBJUqU0MGDBzVq1Ci1aNFCtWvXdmHFAAAgv8hT2ClXrpyz68jV5s2bderUKfXv39+m3cPDQ5s3b9bs2bOVkpKikJAQdevWTePHj3dRpQAAIL+5rZsKnj59Wt9++63OnTunzMxMm3XDhw93SGH/9thjj9mcQrshJCQky92TAQAA/s3usLN48WINHjxYHh4eKlGihM39biwWi1PCDgAAwO2yO+xMmDBBEydOVEREhNzc7L4nIQAAwF1ld1q5evWqunfvTtABAAAFgt2JZcCAAVq+fLkzagEAAHA4u09jRUZG6vHHH9eGDRtUq1YtFS5c2Gb9O++847DiAAAA7tRthZ2NGzdavy7i5gnKAAAA+YndYWfWrFlatGiRzZdxAgAA5Fd2z9nx9PRUs2bNnFELAACAw9kddkaMGKG5c+c6oxYAAACHs/s01o8//qitW7dq7dq1qlGjRpYJyrf6VnIAAIC7ye6w4+fnp65duzqjFgAAAIezO+xERUU5ow4AAACn4DbIAADA1Owe2alQoUKu99M5fvz4HRUEAADgSHaHnZEjR9o8TktL04EDB7RhwwaNHj3aUXUBAAA4hN1hZ8SIEdm2z5s3T3v37r3jggAAABzJYXN22rdvr6+++spRmwMAAHAIh4WdFStWyN/f31GbAwAAcAi7T2PVq1fPZoKyYRiKj4/X+fPn9cEHHzi0OAAAgDtld9jp0qWLzWM3NzcFBASoZcuWqlatmqPqgguVH7fO1SUAAOAwdoedSZMmOaMOAAAAp+CmggAAwNTyPLLj5uaW680EJclisSg9Pf2OiwIAAHCUPIedVatW5bhu9+7dmjNnjjIzMx1SFAAAgKPkOew88cQTWdqOHDmicePG6euvv1avXr00depUhxYHAABwp25rzs7p06c1cOBA1apVS+np6YqJidGSJUtUrlw5R9cHAABwR+wKO4mJiRo7dqwqV66sQ4cOacuWLfr6669Vs2ZNZ9UHAABwR/J8GmvGjBmaPn26goKC9Nlnn2V7WgsAACC/sRiGYeSlo5ubm4oUKaK2bdvK3d09x34rV650WHF3S1JSknx9fZWYmCgfHx9Xl+Ny3FQQAPKvE9M6urqEfCOvf7/zPLLTu3fvW156DgAAkN/kOewsXrzYiWUAAAA4B3dQBgAApkbYAQAApkbYAQAApkbYAQAApkbYAQAAppavw87kyZNlsVhslmrVqlnXX7t2TUOHDlWJEiVUvHhxdevWTWfPnnVhxQAAIL/J12FHkmrUqKEzZ85Yl2+//da6btSoUfr666+1fPly7dixQ6dPn1bXrl1dWC0AAMhv8nyfHVcpVKiQgoKCsrQnJiZq4cKFWrZsmVq3bi1JioqK0oMPPqgffvhBDz/88N0uFQAA5EP5fmTn6NGjCg4OVsWKFdWrVy+dOnVKkrRv3z6lpaWpbdu21r7VqlVT2bJltXv37ly3mZqaqqSkJJsFAACYU74OO40bN9bixYu1YcMGzZ8/X3FxcQoNDdWVK1cUHx8vDw8P+fn52TynVKlSio+Pz3W7kZGR8vX1tS4hISFOfBcAAMCV8vVprPbt21t/rl27tho3bqxy5crpyy+/VJEiRW57uxEREQoPD7c+TkpKIvAAAGBS+Xpk52Z+fn564IEHdOzYMQUFBen69etKSEiw6XP27Nls5/j8m6enp3x8fGwWAABgTgUq7CQnJys2NlalS5dWgwYNVLhwYW3ZssW6/siRIzp16pSaNGniwioBAEB+kq9PY7366qvq1KmTypUrp9OnT2vSpElyd3dXjx495OvrqwEDBig8PFz+/v7y8fHRyy+/rCZNmnAlFgAAsMrXYeevv/5Sjx49dPHiRQUEBKh58+b64YcfFBAQIEl699135ebmpm7duik1NVVhYWH64IMPXFw1AADITyyGYRiuLsLVkpKS5Ovrq8TERObvSCo/bp2rSwAA5ODEtI6uLiHfyOvf7wI1ZwcAAMBehB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBq+TrsREZG6qGHHpK3t7cCAwPVpUsXHTlyxKZPy5YtZbFYbJYXX3zRRRUDAID8Jl+HnR07dmjo0KH64YcfFB0drbS0ND322GNKSUmx6Tdw4ECdOXPGusyYMcNFFQMAgPymkKsLyM2GDRtsHi9evFiBgYHat2+fWrRoYW0vWrSogoKC7nZ5AACgAMjXIzs3S0xMlCT5+/vbtH/66acqWbKkatasqYiICF29ejXX7aSmpiopKclmAQAA5pSvR3b+LTMzUyNHjlSzZs1Us2ZNa3vPnj1Vrlw5BQcH6+DBgxo7dqyOHDmilStX5rityMhITZky5W6UDQAAXMxiGIbh6iLy4qWXXtL69ev17bffqkyZMjn227p1q9q0aaNjx46pUqVK2fZJTU1Vamqq9XFSUpJCQkKUmJgoHx8fh9de0JQft87VJQAAcnBiWkdXl5BvJCUlydfX95Z/vwvEyM6wYcO0du1a7dy5M9egI0mNGzeWpFzDjqenpzw9PR1eJwAAyH/yddgxDEMvv/yyVq1ape3bt6tChQq3fE5MTIwkqXTp0k6uDgAAFAT5OuwMHTpUy5Yt05o1a+Tt7a34+HhJkq+vr4oUKaLY2FgtW7ZMHTp0UIkSJXTw4EGNGjVKLVq0UO3atV1cPQAAjlcQpxq4+tRbvg478+fPl/TPjQP/LSoqSn379pWHh4c2b96s2bNnKyUlRSEhIerWrZvGjx/vgmoBAEB+lK/Dzq3mToeEhGjHjh13qRoAAFAQFaj77AAAANiLsAMAAEwtX5/GMoOCOJEMAAAzYWQHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmmnCzrx581S+fHl5eXmpcePG+vHHH11dEgAAyAdMEXa++OILhYeHa9KkSdq/f7/q1KmjsLAwnTt3ztWlAQAAFzNF2HnnnXc0cOBA9evXT9WrV9eCBQtUtGhRLVq0yNWlAQAAFyvk6gLu1PXr17Vv3z5FRERY29zc3NS2bVvt3r072+ekpqYqNTXV+jgxMVGSlJSU5PD6MlOvOnybAAAUJM74+/rv7RqGkWu/Ah92Lly4oIyMDJUqVcqmvVSpUvr999+zfU5kZKSmTJmSpT0kJMQpNQIAcC/zne3c7V+5ckW+vr45ri/wYed2REREKDw83Po4MzNTly5dUokSJWSxWFxYGaR/knpISIj+/PNP+fj4uLoc/AvHJn/j+ORvHB/HMwxDV65cUXBwcK79CnzYKVmypNzd3XX27Fmb9rNnzyooKCjb53h6esrT09Omzc/Pz1kl4jb5+PjwP4R8imOTv3F88jeOj2PlNqJzQ4GfoOzh4aEGDRpoy5Yt1rbMzExt2bJFTZo0cWFlAAAgPyjwIzuSFB4erj59+qhhw4Zq1KiRZs+erZSUFPXr18/VpQEAABczRdh59tlndf78eU2cOFHx8fGqW7euNmzYkGXSMgoGT09PTZo0KcupRrgexyZ/4/jkbxwf17EYt7peCwAAoAAr8HN2AAAAckPYAQAApkbYAQAApkbYAQAApkbYAQAApkbYQb6SmpqqunXrymKxKCYmJte+LVu2lMVisVlefPHFu1PoPcieY3Pt2jUNHTpUJUqUUPHixdWtW7csdzmHY3Tu3Flly5aVl5eXSpcureeff16nT5/O9Tl8du6O2zk2fHacg7CDfGXMmDG3/I6Tfxs4cKDOnDljXWbMmOHE6u5t9hybUaNG6euvv9by5cu1Y8cOnT59Wl27dnVyhfemVq1a6csvv9SRI0f01VdfKTY2Vk899dQtn8dnx/lu59jw2XESA8gnvvnmG6NatWrGoUOHDEnGgQMHcu3/yCOPGCNGjLgrtd3r7Dk2CQkJRuHChY3ly5db2w4fPmxIMnbv3n0Xqr23rVmzxrBYLMb169dz7MNnxzVudWz47DgPIzvIF86ePauBAwdq6dKlKlq0aJ6f9+mnn6pkyZKqWbOmIiIidPXqVSdWeW+y99js27dPaWlpatu2rbWtWrVqKlu2rHbv3u3MUu95ly5d0qeffqqmTZuqcOHCufbls3N35eXY8NlxHsIOXM4wDPXt21cvvviiGjZsmOfn9ezZU5988om2bdumiIgILV26VM8995wTK7333M6xiY+Pl4eHh/z8/GzaS5Uqpfj4eCdUibFjx6pYsWIqUaKETp06pTVr1uTan8/O3WPPseGz4zyEHTjNuHHjskyCvHn5/fffNXfuXF25ckURERF2bX/QoEEKCwtTrVq11KtXL3388cdatWqVYmNjnfSOzMPZxwZ3Jq/H54bRo0frwIED2rRpk9zd3dW7d28ZuXwTEJ+d2+fsYwPn4Lux4DTnz5/XxYsXc+1TsWJFPfPMM/r6669lsVis7RkZGXJ3d1evXr20ZMmSPL1eSkqKihcvrg0bNigsLOyOajc7Zx6brVu3qk2bNrp8+bLNv1DLlSunkSNHatSoUQ57H2aV1+Pj4eGRpf2vv/5SSEiIvv/+ezVp0iRPr8dnJ++ceWz47DiPKb71HPlTQECAAgICbtlvzpw5euONN6yPT58+rbCwMH3xxRdq3Lhxnl/vxuXQpUuXtrvWe40zj02DBg1UuHBhbdmyRd26dZMkHTlyRKdOncrzH997XV6PT3YyMzMl/XOrgLzis5N3zjw2fHacyJWzo4HsxMXFZbni56+//jKqVq1q7NmzxzAMwzh27JgxdepUY+/evUZcXJyxZs0ao2LFikaLFi1cVPW9IS/HxjAM48UXXzTKli1rbN261di7d6/RpEkTo0mTJi6o2Nx++OEHY+7cucaBAweMEydOGFu2bDGaNm1qVKpUybh27ZphGHx2XOV2jo1h8NlxFkZ2UCCkpaXpyJEj1itGPDw8tHnzZs2ePVspKSkKCQlRt27dNH78eBdXeu+5+dhI0rvvvis3Nzd169ZNqampCgsL0wcffODCKs2paNGiWrlypSZNmqSUlBSVLl1a7dq10/jx4+Xp6SmJz46r3M6xkfjsOAtzdgAAgKlxNRYAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1/weS8zOpQbPu+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MjAyFhoY6fJluQQg7+v+/W8bX15ewAwBAKfNXS1BYoAwAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACzNrWEnPj5et99+uypVqqTAwEB17dpV+/btcxhz9913y2azOTyeeuophzHHjh1Tly5dVL58eQUGBmrEiBHKycm5kVMBAAAllFvvoJyYmKjY2FjdfvvtysnJ0fPPP6/27dtrz549qlChgn3cgAEDNHnyZPvz8uXL23/Ozc1Vly5dFBwcrO+//16nTp1Snz59VLZsWb300ks3dD4AAKDksRljjLuLuOLMmTMKDAxUYmKi2rZtK+n3IztNmzbVzJkzC3zN8uXL9c9//lMnT55UUFCQJGnOnDkaNWqUzpw5Iy8vr3yvycrKUlZWlv35le/WSE9P5+siAAAoJTIyMuTn5/eXf79L1Jqd9PR0SVJAQIBD+wcffKCqVauqYcOGGjNmjC5dumTvS0pKUqNGjexBR5I6dOigjIwM7d69u8D3iY+Pl5+fn/3BN54DAGBdJeaLQPPy8jR06FC1adNGDRs2tLf/3//9n8LCwhQSEqIdO3Zo1KhR2rdvnz7//HNJUnJyskPQkWR/npycXOB7jRkzRnFxcfbnV47sAAAA6ykxYSc2Nla7du3S+vXrHdoHDhxo/7lRo0aqXr262rVrp0OHDql27drX9V7e3t7y9vYuUr0AAKB0KBFhZ9CgQVq6dKnWrVunm2+++ZpjW7ZsKUk6ePCgateureDgYG3cuNFhzOnTpyVJwcHBxVMw4AI1Ry9zdwnX5ei0Lu4uAQCc4tY1O8YYDRo0SIsXL9aaNWsUHh7+l6/Ztm2bJKl69eqSpMjISO3cuVMpKSn2MQkJCfL19VVERESx1A0AAEoPtx7ZiY2N1cKFC/XFF1+oUqVK9jU2fn5+8vHx0aFDh7Rw4UJ17txZVapU0Y4dOzRs2DC1bdtWjRs3liS1b99eERER6t27t6ZPn67k5GS98MILio2N5VQVAABw75Gd2bNnKz09XXfffbeqV69uf3z88ceSJC8vL61atUrt27dXvXr1NHz4cHXr1k1ffvmlfRuenp5aunSpPD09FRkZqccff1x9+vRxuC8PAAD4+3LrkZ2/usVPaGioEhMT/3I7YWFh+uqrr1xVFgAAsJASdZ8dAAAAVyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyvj7gIAlC41Ry9zdwlOOzqti7tLAOBGHNkBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACW5tawEx8fr9tvv12VKlVSYGCgunbtqn379jmM+e233xQbG6sqVaqoYsWK6tatm06fPu0w5tixY+rSpYvKly+vwMBAjRgxQjk5OTdyKgAAoIRya9hJTExUbGysfvjhByUkJCg7O1vt27fXxYsX7WOGDRumL7/8UosWLVJiYqJOnjyphx9+2N6fm5urLl266PLly/r++++1YMECzZ8/X+PHj3fHlAAAQAljM8YYdxdxxZkzZxQYGKjExES1bdtW6enpqlatmhYuXKhHHnlEkvTTTz+pfv36SkpKUqtWrbR8+XL985//1MmTJxUUFCRJmjNnjkaNGqUzZ87Iy8sr3/tkZWUpKyvL/jwjI0OhoaFKT0+Xr6/vjZks/vZqjl7m7hL+No5O6+LuEgAUg4yMDPn5+f3l3+8StWYnPT1dkhQQECBJ2rx5s7KzsxUVFWUfU69ePdWoUUNJSUmSpKSkJDVq1MgedCSpQ4cOysjI0O7duwt8n/j4ePn5+dkfoaGhxTUlAADgZiUm7OTl5Wno0KFq06aNGjZsKElKTk6Wl5eX/P39HcYGBQUpOTnZPuaPQedK/5W+gowZM0bp6en2x/Hjx108GwAAUFKUcXcBV8TGxmrXrl1av359sb+Xt7e3vL29i/19AACA+5WIIzuDBg3S0qVLtXbtWt1888329uDgYF2+fFlpaWkO40+fPq3g4GD7mD9fnXXl+ZUxAADg78utYccYo0GDBmnx4sVas2aNwsPDHfqbN2+usmXLavXq1fa2ffv26dixY4qMjJQkRUZGaufOnUpJSbGPSUhIkK+vryIiIm7MRAAAQInl1tNYsbGxWrhwob744gtVqlTJvsbGz89PPj4+8vPzU//+/RUXF6eAgAD5+vrq2WefVWRkpFq1aiVJat++vSIiItS7d29Nnz5dycnJeuGFFxQbG8upKgAA4N6wM3v2bEnS3Xff7dA+b948xcTESJJmzJghDw8PdevWTVlZWerQoYPefvtt+1hPT08tXbpUTz/9tCIjI1WhQgVFR0dr8uTJN2oaAACgBCtR99lxl8Jepw+4EvfZuXG4zw5gTaXyPjsAAACuRtgBAACWVmLuswMUBaeEAABXw5EdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaU6HnS1btmjnzp3251988YW6du2q559/XpcvX3ZpcQAAAEXldNh58skntX//fknS4cOH1aNHD5UvX16LFi3SyJEjXV4gAABAUTgddvbv36+mTZtKkhYtWqS2bdtq4cKFmj9/vj777DNX1wcAAFAkTocdY4zy8vIkSatWrVLnzp0lSaGhoTp79qxrqwMAACgip8NOixYtNHXqVL3//vtKTExUly5dJElHjhxRUFCQywsEAAAoCqfDzsyZM7VlyxYNGjRIY8eOVZ06dSRJn376qVq3bu3yAgEAAIqijLMvaNy4scPVWFe88sor8vT0dElRAAAArnJd99lJS0vTO++8ozFjxig1NVWStGfPHqWkpLi0OAAAgKJy+sjOjh071K5dO/n7++vo0aMaMGCAAgIC9Pnnn+vYsWP6z3/+Uxx1AgAAXBenj+zExcWpb9++OnDggMqVK2dv79y5s9atW+fS4gAAAIrK6bDz448/6sknn8zXftNNNyk5OdklRQEAALiK02HH29tbGRkZ+dr379+vatWquaQoAAAAV3E67DzwwAOaPHmysrOzJUk2m03Hjh3TqFGj1K1bN5cXCAAAUBROh53XXntNmZmZCgwM1K+//qq77rpLderUUaVKlfTiiy8WR40AAADXzemrsfz8/JSQkKDvvvtO27dvV2Zmppo1a6aoqKjiqA8AAKBInA47V7Rp00Zt2rRxZS0AAAAu5/RprMGDB2vWrFn52t98800NHTrUFTUBAAC4jNNh57PPPivwiE7r1q316aefuqQoAAAAV3E67Jw7d05+fn752n19fXX27FmXFAUAAOAqToedOnXqaMWKFfnaly9frlq1armkKAAAAFdxeoFyXFycBg0apDNnzujee++VJK1evVqvvfaaZs6c6er6AAAAisTpsNOvXz9lZWXpxRdf1JQpUyRJNWvW1OzZs9WnTx+XFwgAAFAU13Xp+dNPP62nn35aZ86ckY+PjypWrOjqugAAAFziuu+zI4nvwgIAACWe0wuUT58+rd69eyskJERlypSRp6enwwMAAKAkcfrITkxMjI4dO6Zx48apevXqstlsxVEXAACASzgddtavX69vv/1WTZs2LYZyAAAAXMvp01ihoaEyxhRHLQAAAC7ndNiZOXOmRo8eraNHjxZDOQAAAK7l9Gms7t2769KlS6pdu7bKly+vsmXLOvSnpqa6rDgAAICicjrscJdkAABQmjgddqKjo4ujDgAAgGLh9JodSTp06JBeeOEF9ezZUykpKZJ+/yLQ3bt3u7Q4AACAonI67CQmJqpRo0basGGDPv/8c2VmZkqStm/frgkTJri8QAAAgKJwOuyMHj1aU6dOVUJCgry8vOzt9957r3744QeXFgcAAFBUToednTt36qGHHsrXHhgYqLNnz7qkKAAAAFdxOuz4+/vr1KlT+dq3bt2qm266ySVFAQAAuIrTYadHjx4aNWqUkpOTZbPZlJeXp++++07PPfec+vTpUxw1AgAAXDenw85LL72kevXqKTQ0VJmZmYqIiFDbtm3VunVrvfDCC8VRIwAAwHVz6j47xhglJydr1qxZGj9+vHbu3KnMzEzddtttqlu3bnHVCAAAcN2cDjt16tTR7t27VbduXYWGhhZXXQAAAC7h1GksDw8P1a1bV+fOnSuuegAAAFzK6TU706ZN04gRI7Rr167iqAcAAMClnP5urD59+ujSpUtq0qSJvLy85OPj49DPt54DAICShG89BwAAluZU2MnOzlZiYqLGjRun8PDw4qoJAADAZZxas1O2bFl99tlnxVULAACAyzm9QLlr165asmRJMZQCAADgek6v2albt64mT56s7777Ts2bN1eFChUc+gcPHuyy4gAAAIrK6SM77777rvz9/bV582bNnTtXM2bMsD+cXby8bt063X///QoJCZHNZst3xCgmJkY2m83h0bFjR4cxqamp6tWrl3x9feXv76/+/fsrMzPT2WkBAACLcvrIzpEjR1z25hcvXlSTJk3Ur18/PfzwwwWO6dixo+bNm2d/7u3t7dDfq1cvnTp1SgkJCcrOzlbfvn01cOBALVy40GV1AgCA0svpsONKnTp1UqdOna45xtvbW8HBwQX27d27VytWrNCPP/6oFi1aSJLeeOMNde7cWa+++qpCQkIKfF1WVpaysrLszzMyMq5zBgAAoKRzOuz069fvmv3vvffedRdTkG+++UaBgYGqXLmy7r33Xk2dOlVVqlSRJCUlJcnf398edCQpKipKHh4e2rBhgx566KECtxkfH69Jkya5tE4AAFAyOR12zp8/7/A8Oztbu3btUlpamu69916XFSb9fgrr4YcfVnh4uA4dOqTnn39enTp1UlJSkjw9PZWcnKzAwECH15QpU0YBAQFKTk6+6nbHjBmjuLg4+/OMjAy+1BQAAItyOuwsXrw4X1teXp6efvpp1a5d2yVFXdGjRw/7z40aNVLjxo1Vu3ZtffPNN2rXrt11b9fb2zvf2h8AAGBNTl+NVeBGPDwUFxenGTNmuGJzV1WrVi1VrVpVBw8elCQFBwcrJSXFYUxOTo5SU1Ovus4HAAD8vbgk7EjSoUOHlJOT46rNFeiXX37RuXPnVL16dUlSZGSk0tLStHnzZvuYNWvWKC8vTy1btizWWgAAQOng9GmsP651kSRjjE6dOqVly5YpOjraqW1lZmbaj9JIv1/Wvm3bNgUEBCggIECTJk1St27dFBwcrEOHDmnkyJGqU6eOOnToIEmqX7++OnbsqAEDBmjOnDnKzs7WoEGD1KNHj6teiQUAAP5enA47W7dudXju4eGhatWq6bXXXvvLK7X+bNOmTbrnnnvsz68EqejoaM2ePVs7duzQggULlJaWppCQELVv315TpkxxWG/zwQcfaNCgQWrXrp08PDzUrVs3zZo1y9lpAQAAi7IZY4y7i3C3jIwM+fn5KT09Xb6+vu4uB9eh5uhl7i4BJdjRaV3cXQKAYlDYv99Or9k5cuSIDhw4kK/9wIEDOnr0qLObAwAAKFZOh52YmBh9//33+do3bNigmJgYV9QEAADgMk6Hna1bt6pNmzb52lu1aqVt27a5oiYAAACXcTrs2Gw2XbhwIV97enq6cnNzXVIUAACAqzgddtq2bav4+HiHYJObm6v4+HjdeeedLi0OAACgqJy+9Pzll19W27Ztdeutt+of//iHJOnbb79VRkaG1qxZ4/ICAaCoSuPVelxBBriO00d2IiIitGPHDj322GNKSUnRhQsX1KdPH/30009q2LBhcdQIAABw3Zw+siNJISEheumll1xdCwAAgMs5fWRn3rx5WrRoUb72RYsWacGCBS4pCgAAwFWcDjvx8fGqWrVqvvbAwECO9gAAgBLH6bBz7NgxhYeH52sPCwvTsWPHXFIUAACAqzgddgIDA7Vjx4587du3b1eVKlVcUhQAAICrOB12evbsqcGDB2vt2rXKzc1Vbm6u1qxZoyFDhqhHjx7FUSMAAMB1c/pqrClTpujo0aNq166dypT5/eV5eXnq06cPa3YAAECJ43TY8fLy0scff6wpU6Zo+/bt8vHxUaNGjRQWFlYc9QEAABTJdd1nR5ICAgJ0zz33FHhlFgAAQEnh1JqdtLQ0xcbGqmrVqgoKClJQUJCqVq2qQYMGKS0trZhKBAAAuH6FPrKTmpqqyMhInThxQr169VL9+vUlSXv27NH8+fO1evVqff/996pcuXKxFQsAAOCsQoedyZMny8vLS4cOHVJQUFC+vvbt22vy5MmaMWOGy4sEAAC4XoU+jbVkyRK9+uqr+YKOJAUHB2v69OlavHixS4sDAAAoqkKHnVOnTqlBgwZX7W/YsKGSk5NdUhQAAICrFDrsVK1aVUePHr1q/5EjRxQQEOCKmgAAAFym0GGnQ4cOGjt2rC5fvpyvLysrS+PGjVPHjh1dWhwAAEBRObVAuUWLFqpbt65iY2NVr149GWO0d+9evf3228rKytL7779fnLUCAAA4rdBh5+abb1ZSUpKeeeYZjRkzRsYYSZLNZtN9992nN998U6GhocVWKAAAwPVw6g7K4eHhWr58uc6fP68DBw5IkurUqcNaHQAAUGJd19dFVK5cWXfccYerawEAAHA5p74uAgAAoLQh7AAAAEsj7AAAAEsrVNhp1qyZzp8/L+n3S9AvXbpUrEUBAAC4SqHCzt69e3Xx4kVJ0qRJk5SZmVmsRQEAALhKoa7Gatq0qfr27as777xTxhi9+uqrqlixYoFjx48f79ICAQAAiqJQYWf+/PmaMGGCli5dKpvNpuXLl6tMmfwvtdlshB0AAFCiFCrs3Hrrrfroo48kSR4eHlq9erUCAwOLtTAAAABXcPqmgnl5ecVRBwAAQLG4rjsoHzp0SDNnztTevXslSRERERoyZIhq167t0uIAAACKyun77KxcuVIRERHauHGjGjdurMaNG2vDhg1q0KCBEhISiqNGAACA6+b0kZ3Ro0dr2LBhmjZtWr72UaNG6b777nNZcQAAAEXl9JGdvXv3qn///vna+/Xrpz179rikKAAAAFdxOuxUq1ZN27Zty9e+bds2rtACAAAljtOnsQYMGKCBAwfq8OHDat26tSTpu+++08svv6y4uDiXFwgAAFAUToedcePGqVKlSnrttdc0ZswYSVJISIgmTpyowYMHu7xAAACAonA67NhsNg0bNkzDhg3ThQsXJEmVKlVyeWEAAACucF332bmCkAMAAEo6pxcoAwAAlCaEHQAAYGmEHQAAYGlOhZ3s7Gy1a9dOBw4cKK56AAAAXMqpsFO2bFnt2LGjuGoBAABwOadPYz3++ON69913i6MWAAAAl3P60vOcnBy99957WrVqlZo3b64KFSo49L/++usuKw4AAKConA47u3btUrNmzSRJ+/fvd+iz2WyuqQoAAMBFnA47a9euLY46AAAAisV1X3p+8OBBrVy5Ur/++qskyRjjsqIAAABcxemwc+7cObVr10633HKLOnfurFOnTkmS+vfvr+HDh7u8QAAAgKJwOuwMGzZMZcuW1bFjx1S+fHl7e/fu3bVixQqXFgcAAFBUTq/Z+frrr7Vy5UrdfPPNDu1169bVzz//7LLCAAAAXMHpIzsXL150OKJzRWpqqry9vV1SFAAAgKs4HXb+8Y9/6D//+Y/9uc1mU15enqZPn6577rnHqW2tW7dO999/v0JCQmSz2bRkyRKHfmOMxo8fr+rVq8vHx0dRUVH5vqoiNTVVvXr1kq+vr/z9/dW/f39lZmY6Oy0AAGBRToed6dOna+7cuerUqZMuX76skSNHqmHDhlq3bp1efvllp7Z18eJFNWnSRG+99dZV32vWrFmaM2eONmzYoAoVKqhDhw767bff7GN69eql3bt3KyEhQUuXLtW6des0cOBAZ6cFAAAsymau45rx9PR0vfnmm9q+fbsyMzPVrFkzxcbGqnr16tdfiM2mxYsXq2vXrpJ+P6oTEhKi4cOH67nnnrO/b1BQkObPn68ePXpo7969ioiI0I8//qgWLVpIklasWKHOnTvrl19+UUhISKHeOyMjQ35+fkpPT5evr+91zwHuU3P0MneXALjU0Wld3F0CUOIV9u+30wuUJcnPz09jx4697uIK48iRI0pOTlZUVJTD+7Zs2VJJSUnq0aOHkpKS5O/vbw86khQVFSUPDw9t2LBBDz30UIHbzsrKUlZWlv15RkZG8U0EAAC41XWFnfPnz+vdd9/V3r17JUkRERHq27evAgICXFZYcnKyJCkoKMihPSgoyN6XnJyswMBAh/4yZcooICDAPqYg8fHxmjRpkstqBQAAJZfTa3bWrVunmjVratasWTp//rzOnz+vWbNmKTw8XOvWrSuOGl1uzJgxSk9Ptz+OHz/u7pIAAEAxcfrITmxsrLp3767Zs2fL09NTkpSbm6tnnnlGsbGx2rlzp0sKCw4OliSdPn3aYS3Q6dOn1bRpU/uYlJQUh9fl5OQoNTXV/vqCeHt7c5k8AAB/E04f2Tl48KCGDx9uDzqS5Onpqbi4OB08eNBlhYWHhys4OFirV6+2t2VkZGjDhg2KjIyUJEVGRiotLU2bN2+2j1mzZo3y8vLUsmVLl9UCAABKL6eP7DRr1kx79+7Vrbfe6tC+d+9eNWnSxKltZWZmOgSkI0eOaNu2bQoICFCNGjU0dOhQTZ06VXXr1lV4eLjGjRunkJAQ+xVb9evXV8eOHTVgwADNmTNH2dnZGjRokHr06FHoK7EAAIC1FSrs7Nixw/7z4MGDNWTIEB08eFCtWrWSJP3www966623NG3aNKfefNOmTQ43IoyLi5MkRUdHa/78+Ro5cqQuXryogQMHKi0tTXfeeadWrFihcuXK2V/zwQcfaNCgQWrXrp08PDzUrVs3zZo1y6k6AACAdRXqPjseHh6y2Wz6q6E2m025ubkuK+5G4T47pR/32YHVcJ8d4K+59D47R44ccVlhAAAAN1Khwk5YWFhx1wEAAFAsruumgidPntT69euVkpKivLw8h77Bgwe7pDAAAABXcDrszJ8/X08++aS8vLxUpUoV2Ww2e5/NZiPsAACAEsXpsDNu3DiNHz9eY8aMkYeH07fpAQAAuKGcTiuXLl1Sjx49CDoAAKBUcDqx9O/fX4sWLSqOWgAAAFzO6dNY8fHx+uc//6kVK1aoUaNGKlu2rEP/66+/7rLiAAAAiuq6ws7KlSvtXxfx5wXKAAAAJYnTYee1117Te++9p5iYmGIoBwAAwLWcXrPj7e2tNm3aFEctAAAALud02BkyZIjeeOON4qgFAADA5Zw+jbVx40atWbNGS5cuVYMGDfItUP78889dVhwAAEBROR12/P399fDDDxdHLQAAAC7ndNiZN29ecdQBAABQLLgNMgAAsDSnj+yEh4df8346hw8fLlJBAAAAruR02Bk6dKjD8+zsbG3dulUrVqzQiBEjXFUXAACASzgddoYMGVJg+1tvvaVNmzYVuSAAAABXctmanU6dOumzzz5z1eYAAABcwmVh59NPP1VAQICrNgcAAOASTp/Guu222xwWKBtjlJycrDNnzujtt992aXEAAABF5XTY6dq1q8NzDw8PVatWTXfffbfq1avnqroAAABcwumwM2HChOKoAyVIzdHL3F0CAAAuw00FAQCApRX6yI6Hh8c1byYoSTabTTk5OUUuCgAAwFUKHXYWL1581b6kpCTNmjVLeXl5LikKAADAVQoddh588MF8bfv27dPo0aP15ZdfqlevXpo8ebJLiwMAACiq61qzc/LkSQ0YMECNGjVSTk6Otm3bpgULFigsLMzV9QEAABSJU2EnPT1do0aNUp06dbR7926tXr1aX375pRo2bFhc9QEAABRJoU9jTZ8+XS+//LKCg4P14YcfFnhaCwAAoKSxGWNMYQZ6eHjIx8dHUVFR8vT0vOq4zz//3GXF3SgZGRny8/NTenq6fH193V2O23GfHcD9jk7r4u4SgBKvsH+/C31kp0+fPn956TkAAEBJU+iwM3/+/GIsAwAAoHhwB2UAAGBpTn83FgCg+JXGtXOsM0JJxZEdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaSU67EycOFE2m83hUa9ePXv/b7/9ptjYWFWpUkUVK1ZUt27ddPr0aTdWDAAASpoSHXYkqUGDBjp16pT9sX79envfsGHD9OWXX2rRokVKTEzUyZMn9fDDD7uxWgAAUNKUcXcBf6VMmTIKDg7O156enq53331XCxcu1L333itJmjdvnurXr68ffvhBrVq1utGlAgCAEqjEH9k5cOCAQkJCVKtWLfXq1UvHjh2TJG3evFnZ2dmKioqyj61Xr55q1KihpKSka24zKytLGRkZDg8AAGBNJTrstGzZUvPnz9eKFSs0e/ZsHTlyRP/4xz904cIFJScny8vLS/7+/g6vCQoKUnJy8jW3Gx8fLz8/P/sjNDS0GGcBAADcqUSfxurUqZP958aNG6tly5YKCwvTJ598Ih8fn+ve7pgxYxQXF2d/npGRQeABAMCiSvSRnT/z9/fXLbfcooMHDyo4OFiXL19WWlqaw5jTp08XuMbnj7y9veXr6+vwAAAA1lSqwk5mZqYOHTqk6tWrq3nz5ipbtqxWr15t79+3b5+OHTumyMhIN1YJAABKkhJ9Guu5557T/fffr7CwMJ08eVITJkyQp6enevbsKT8/P/Xv319xcXEKCAiQr6+vnn32WUVGRnIlFgAAsCvRYeeXX35Rz549de7cOVWrVk133nmnfvjhB1WrVk2SNGPGDHl4eKhbt27KyspShw4d9Pbbb7u5agAAUJLYjDHG3UW4W0ZGhvz8/JSens76HUk1Ry9zdwkASqGj07q4uwT8zRT273epWrMDAADgLMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwtDLuLsDqao5e5u4SAOCGKI2/745O6+LuEnADcGQHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYWhl3FwAAgLvUHL3M3SU47ei0Lu4uodThyA4AALA0wg4AALA0wg4AALA0y4Sdt956SzVr1lS5cuXUsmVLbdy40d0lAQCAEsASYefjjz9WXFycJkyYoC1btqhJkybq0KGDUlJS3F0aAABwM0uEnddff10DBgxQ3759FRERoTlz5qh8+fJ677333F0aAABws1J/6fnly5e1efNmjRkzxt7m4eGhqKgoJSUlFfiarKwsZWVl2Z+np6dLkjIyMlxeX17WJZdvEwDw91Vj2CJ3l+C0XZM6FMt2r/zdNsZcc1ypDztnz55Vbm6ugoKCHNqDgoL0008/Ffia+Ph4TZo0KV97aGhosdQIAMDfmd/M4t3+hQsX5Ofnd9X+Uh92rseYMWMUFxdnf56Xl6fU1FRVqVJFNpvN3p6RkaHQ0FAdP35cvr6+7ii1WDG/0o35lW7Mr3RjfiWDMUYXLlxQSEjINceV+rBTtWpVeXp66vTp0w7tp0+fVnBwcIGv8fb2lre3t0Obv7//Vd/D19e3RO/somJ+pRvzK92YX+nG/NzvWkd0rij1C5S9vLzUvHlzrV692t6Wl5en1atXKzIy0o2VAQCAkqDUH9mRpLi4OEVHR6tFixa64447NHPmTF28eFF9+/Z1d2kAAMDNLBF2unfvrjNnzmj8+PFKTk5W06ZNtWLFinyLlp3l7e2tCRMm5DvlZRXMr3RjfqUb8yvdmF/pYjN/db0WAABAKVbq1+wAAABcC2EHAABYGmEHAABYGmEHAABYGmEHAABY2t8y7Kxbt07333+/QkJCZLPZtGTJEod+Y4zGjx+v6tWry8fHR1FRUTpw4IDDmNTUVPXq1Uu+vr7y9/dX//79lZmZeQNnUbD4+HjdfvvtqlSpkgIDA9W1a1ft27fPYcxvv/2m2NhYValSRRUrVlS3bt3y3YH62LFj6tKli8qXL6/AwECNGDFCOTk5N3IqBZo9e7YaN25sv6tnZGSkli9fbu8vzXMryLRp02Sz2TR06FB7W2me48SJE2Wz2Rwe9erVs/eX5rldceLECT3++OOqUqWKfHx81KhRI23atMneX5p/v9SsWTPf/rPZbIqNjZVU+vdfbm6uxo0bp/DwcPn4+Kh27dqaMmWKw5dMlub9J/3+HVJDhw5VWFiYfHx81Lp1a/3444/2/tI+v6syf0NfffWVGTt2rPn888+NJLN48WKH/mnTphk/Pz+zZMkSs337dvPAAw+Y8PBw8+uvv9rHdOzY0TRp0sT88MMP5ttvvzV16tQxPXv2vMEzya9Dhw5m3rx5ZteuXWbbtm2mc+fOpkaNGiYzM9M+5qmnnjKhoaFm9erVZtOmTaZVq1amdevW9v6cnBzTsGFDExUVZbZu3Wq++uorU7VqVTNmzBh3TMnB//73P7Ns2TKzf/9+s2/fPvP888+bsmXLml27dhljSvfc/mzjxo2mZs2apnHjxmbIkCH29tI8xwkTJpgGDRqYU6dO2R9nzpyx95fmuRljTGpqqgkLCzMxMTFmw4YN5vDhw2blypXm4MGD9jGl+fdLSkqKw75LSEgwkszatWuNMaV//7344oumSpUqZunSpebIkSNm0aJFpmLFiuZf//qXfUxp3n/GGPPYY4+ZiIgIk5iYaA4cOGAmTJhgfH19zS+//GKMKf3zu5q/Zdj5oz+Hnby8PBMcHGxeeeUVe1taWprx9vY2H374oTHGmD179hhJ5scff7SPWb58ubHZbObEiRM3rPbCSElJMZJMYmKiMeb3uZQtW9YsWrTIPmbv3r1GkklKSjLG/B4GPTw8THJysn3M7Nmzja+vr8nKyrqxEyiEypUrm3feecdSc7tw4YKpW7euSUhIMHfddZc97JT2OU6YMME0adKkwL7SPjdjjBk1apS58847r9pvtd8vQ4YMMbVr1zZ5eXmW2H9dunQx/fr1c2h7+OGHTa9evYwxpX//Xbp0yXh6epqlS5c6tDdr1syMHTu21M/vWv6Wp7Gu5ciRI0pOTlZUVJS9zc/PTy1btlRSUpIkKSkpSf7+/mrRooV9TFRUlDw8PLRhw4YbXvO1pKenS5ICAgIkSZs3b1Z2drbD/OrVq6caNWo4zK9Ro0YOd6Du0KGDMjIytHv37htY/bXl5ubqo48+0sWLFxUZGWmpucXGxqpLly4Oc5Gssf8OHDigkJAQ1apVS7169dKxY8ckWWNu//vf/9SiRQs9+uijCgwM1G233aZ///vf9n4r/X65fPmy/vvf/6pfv36y2WyW2H+tW7fW6tWrtX//fknS9u3btX79enXq1ElS6d9/OTk5ys3NVbly5RzafXx8tH79+lI/v2uxxNdFuFJycrIk5fuqiaCgIHtfcnKyAgMDHfrLlCmjgIAA+5iSIC8vT0OHDlWbNm3UsGFDSb/X7uXlle9b3v88v4Lmf6XP3Xbu3KnIyEj99ttvqlixohYvXqyIiAht27at1M9Nkj766CNt2bLF4Tz6FaV9/7Vs2VLz58/XrbfeqlOnTmnSpEn6xz/+oV27dpX6uUnS4cOHNXv2bMXFxen555/Xjz/+qMGDB8vLy0vR0dGW+v2yZMkSpaWlKSYmRlLp/3dTkkaPHq2MjAzVq1dPnp6eys3N1YsvvqhevXpJKv1/HypVqqTIyEhNmTJF9evXV1BQkD788EMlJSWpTp06pX5+10LYsbDY2Fjt2rVL69evd3cpLnXrrbdq27ZtSk9P16effqro6GglJia6uyyXOH78uIYMGaKEhIR8//dlBVf+D1mSGjdurJYtWyosLEyffPKJfHx83FiZa+Tl5alFixZ66aWXJEm33Xabdu3apTlz5ig6OtrN1bnWu+++q06dOikkJMTdpbjMJ598og8++EALFy5UgwYNtG3bNg0dOlQhISGW2X/vv/+++vXrp5tuukmenp5q1qyZevbsqc2bN7u7tGLFaaw/CQ4OlqR8VxCcPn3a3hccHKyUlBSH/pycHKWmptrHuNugQYO0dOlSrV27VjfffLO9PTg4WJcvX1ZaWprD+D/Pr6D5X+lzNy8vL9WpU0fNmzdXfHy8mjRpon/961+WmNvmzZuVkpKiZs2aqUyZMipTpowSExM1a9YslSlTRkFBQaV+jn/k7++vW265RQcPHrTE/qtevboiIiIc2urXr28/VWeV3y8///yzVq1apSeeeMLeZoX9N2LECI0ePVo9evRQo0aN1Lt3bw0bNkzx8fGSrLH/ateurcTERGVmZur48ePauHGjsrOzVatWLUvM72oIO38SHh6u4OBgrV692t6WkZGhDRs2KDIyUpIUGRmptLQ0hyS8Zs0a5eXlqWXLlje85j8yxmjQoEFavHix1qxZo/DwcIf+5s2bq2zZsg7z27dvn44dO+Ywv507dzr8C52QkCBfX998v8hLgry8PGVlZVlibu3atdPOnTu1bds2+6NFixbq1auX/efSPsc/yszM1KFDh1S9enVL7L82bdrku9XD/v37FRYWJqn0/365Yt68eQoMDFSXLl3sbVbYf5cuXZKHh+OfRU9PT+Xl5Umyzv6TpAoVKqh69eo6f/68Vq5cqQcffNBS88vH3Suk3eHChQtm69atZuvWrUaSef31183WrVvNzz//bIz5/dI7f39/88UXX5gdO3aYBx98sMBL72677TazYcMGs379elO3bt0Scend008/bfz8/Mw333zjcInopUuX7GOeeuopU6NGDbNmzRqzadMmExkZaSIjI+39Vy4Pbd++vdm2bZtZsWKFqVatWom4PHT06NEmMTHRHDlyxOzYscOMHj3a2Gw28/XXXxtjSvfcruaPV2MZU7rnOHz4cPPNN9+YI0eOmO+++85ERUWZqlWrmpSUFGNM6Z6bMb/fLqBMmTLmxRdfNAcOHDAffPCBKV++vPnvf/9rH1Oaf78YY0xubq6pUaOGGTVqVL6+0r7/oqOjzU033WS/9Pzzzz83VatWNSNHjrSPKe37b8WKFWb58uXm8OHD5uuvvzZNmjQxLVu2NJcvXzbGlP75Xc3fMuysXbvWSMr3iI6ONsb8fnnhuHHjTFBQkPH29jbt2rUz+/btc9jGuXPnTM+ePU3FihWNr6+v6du3r7lw4YIbZuOooHlJMvPmzbOP+fXXX80zzzxjKleubMqXL28eeughc+rUKYftHD161HTq1Mn4+PiYqlWrmuHDh5vs7OwbPJv8+vXrZ8LCwoyXl5epVq2aadeunT3oGFO653Y1fw47pXmO3bt3N9WrVzdeXl7mpptuMt27d3e4B01pntsVX375pWnYsKHx9vY29erVM3PnznXoL82/X4wxZuXKlUZSvpqNKf37LyMjwwwZMsTUqFHDlCtXztSqVcuMHTvW4bL40r7/Pv74Y1OrVi3j5eVlgoODTWxsrElLS7P3l/b5XY3NmD/cGhIAAMBiWLMDAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAs7f8DIoR+K9dtm0cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tWlXSH78wn3nmGS1atCjP5HFneeSRR1ShQgX169dPw4YNk8Vi0YcffpjnF7Sbm5sWLVqkLl26qEmTJhowYICqVKmiX375Rfv379f69euLpN783HvvvXr44YcVFRWllJQU+fn5admyZcrKynLaPhMTE9W1a1d16NBBcXFx+uijj/T000+rcePGkv4IYFOnTlVUVJSSkpLUvXt3lS9fXomJiVq1apUGDx6c5xEAKDkIOyjxhg0bpi+//FIbNmxQRkaGqlWrpqlTp2r06NHWPk8++aSGDh2qZcuW6aOPPpJhGAoPD5ebm5u+/PJLTZw4UcuXL9eSJUtUvXp1vfHGG9a7lHJ98MEHCgwM1CeffKJVq1apXbt2Wr58uerWrasyZcoUqNalS5dq6NChWrBggQzDUPv27bV27VoFBQU59DO5kXfffVc1a9ZUbGysVq1apcDAQEVFRSk6OrpI9p+ff/3rX3J3d9fHH3+sa9euqXnz5tq0aZN1NE2S/ve//2nEiBHq0qWL+vXrZ23v3bu3PvvsM73yyivq2LFjvpPCHa1ixYpavXq1Ro4cqfHjx6tChQp65pln1LZtW5uapT9GBLdu3aqYmBjNnj1bOTk5CgkJsT53yZU+/vhjPf/885oxY4Z8fX01cOBAtWnTxvpMKkdbvny59VlEpUqV0pAhQ/TGG2/Y9Bk7dqzq1KmjOXPmKCYmRtIfE8bbt2+vrl27OqUu3BkshqvHQ4ESLCEhQffff78++ugj9e7d29XlAIApMWcHKCJXr17N0zZ37ly5ubnd8gnDAIDC4zIWUERmzpyp+Ph4tWnTRqVKldLatWu1du1aDR48OM/zRlD0srOz9fvvv9+0T7ly5fLcwnwnSElJ0fXr12+43N3dPc9zegAz4TIWUEQ2btyomJgYHThwQOnp6brnnnvUp08fjRs3zuaJu3CNpKSkW87ZiY6OtvnS1jtF69attX379hsur1atWp4v9QTMhLADAPrj2Tw7d+68aZ+aNWuqZs2aRVSR48THx9/0acpeXl5q3rx5EVYEFC3CDgAAMDUmKAMAAFNjooD++E6VU6dOqXz58k753h8AAOB4hmHo0qVLCgoKkpvbjcdvCDuSTp06xd0wAADcoU6ePGl9Mnp+CDuS9QscT548KW9vbxdXAwAACiItLU3BwcE2X8ScH8KO/v/vJfL29ibsAABwh7nVFBQmKAMAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMr5eoCAAB5VR/7tatLsFvSjM6uLgHIFyM7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1FwadqZPn66//e1vKl++vPz9/dW9e3cdOnTIpk/r1q1lsVhsXi+88IJNnxMnTqhz584qW7as/P39NXr0aGVlZRXloQAAgGKqlCt3vn37dkVEROhvf/ubsrKy9Oqrr6p9+/Y6cOCA7rrrLmu/QYMGafLkydb3ZcuWtf6cnZ2tzp07KzAwULt27dLp06fVt29flS5dWtOmTSvS4wEAAMWPS8POunXrbN7HxsbK399f8fHxatmypbW9bNmyCgwMzHcbGzZs0IEDB7Rp0yYFBASoSZMmmjJlisaMGaNJkybJw8PDqccAAACKt2I1Zyc1NVWS5OfnZ9P+8ccfq1KlSmrYsKGioqJ05coV67K4uDg1atRIAQEB1rawsDClpaVp//79+e4nIyNDaWlpNi8AAGBOLh3Z+bOcnBwNHz5czZs3V8OGDa3tTz/9tKpVq6agoCDt3btXY8aM0aFDh7Ry5UpJUnJysk3QkWR9n5ycnO++pk+frpiYGCcdCQAAKE6KTdiJiIjQzz//rJ07d9q0Dx482Ppzo0aNVKVKFbVt21bHjh1TSEhIofYVFRWlyMhI6/u0tDQFBwcXrnAAAFCsFYvLWEOGDNHq1au1detWVa1a9aZ9mzVrJkk6evSoJCkwMFBnzpyx6ZP7/kbzfDw9PeXt7W3zAgAA5uTSsGMYhoYMGaJVq1Zpy5YtqlGjxi3XSUhIkCRVqVJFkhQaGqp9+/bp7Nmz1j4bN26Ut7e3GjRo4JS6AQDAncOll7EiIiK0dOlSffHFFypfvrx1jo2Pj4+8vLx07NgxLV26VJ06dVLFihW1d+9ejRgxQi1bttR9990nSWrfvr0aNGigPn36aObMmUpOTtb48eMVEREhT09PVx4eAAAoBlw6srNo0SKlpqaqdevWqlKlivW1fPlySZKHh4c2bdqk9u3bq169eho5cqR69Oihr776yroNd3d3rV69Wu7u7goNDdUzzzyjvn372jyXBwAAlFwuHdkxDOOmy4ODg7V9+/ZbbqdatWpas2aNo8oCAAAmUiwmKAMAADgLYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJhaKVcXAADOVn3s164uAYALMbIDAABMjbADAABMzaVhZ/r06frb3/6m8uXLy9/fX927d9ehQ4ds+ly7dk0RERGqWLGiypUrpx49eujMmTM2fU6cOKHOnTurbNmy8vf31+jRo5WVlVWUhwIAAIopl4ad7du3KyIiQrt379bGjRuVmZmp9u3b6/Lly9Y+I0aM0FdffaUVK1Zo+/btOnXqlJ588knr8uzsbHXu3FnXr1/Xrl279P777ys2NlYTJ050xSEBAIBixmIYhuHqInL9/vvv8vf31/bt29WyZUulpqaqcuXKWrp0qf7xj39Ikn755RfVr19fcXFxevjhh7V27Vo98cQTOnXqlAICAiRJb7/9tsaMGaPff/9dHh4et9xvWlqafHx8lJqaKm9vb6ceI4CixwTlopE0o7OrS0AJU9Df38Vqzk5qaqokyc/PT5IUHx+vzMxMtWvXztqnXr16uueeexQXFydJiouLU6NGjaxBR5LCwsKUlpam/fv357ufjIwMpaWl2bwAAIA5FZuwk5OTo+HDh6t58+Zq2LChJCk5OVkeHh7y9fW16RsQEKDk5GRrnz8HndzlucvyM336dPn4+FhfwcHBDj4aAABQXBSbsBMREaGff/5Zy5Ytc/q+oqKilJqaan2dPHnS6fsEAACuYXfY+fHHH7Vv3z7r+y+++ELdu3fXq6++quvXrxeqiCFDhmj16tXaunWrqlatam0PDAzU9evXdfHiRZv+Z86cUWBgoLXPX+/Oyn2f2+evPD095e3tbfMCAADmZHfYef7553X48GFJ0vHjxxUeHq6yZctqxYoVeuWVV+zalmEYGjJkiFatWqUtW7aoRo0aNsubNm2q0qVLa/Pmzda2Q4cO6cSJEwoNDZUkhYaGat++fTp79qy1z8aNG+Xt7a0GDRrYe3gAAMBk7A47hw8fVpMmTSRJK1asUMuWLbV06VLFxsbqs88+s2tbERER+uijj7R06VKVL19eycnJSk5O1tWrVyVJPj4+GjhwoCIjI7V161bFx8drwIABCg0N1cMPPyxJat++vRo0aKA+ffrop59+0vr16zV+/HhFRETI09PT3sMDAAAmY/d3YxmGoZycHEnSpk2b9MQTT0iSgoODde7cObu2tWjRIklS69atbdqXLFmi/v37S5LmzJkjNzc39ejRQxkZGQoLC9PChQutfd3d3bV69Wq9+OKLCg0N1V133aV+/fpp8uTJ9h4aAAAwIbvDzoMPPqipU6eqXbt22r59uzWwJCYm5rkr6lYK8oifMmXKaMGCBVqwYMEN+1SrVk1r1qyxa98AAKBksPsy1ty5c/Xjjz9qyJAhGjdunGrVqiVJ+vTTT/XII484vEAAAIDbYffIzn333WdzN1auN954Q+7u7g4pCgAAwFEK9Zydixcv6t1331VUVJRSUlIkSQcOHLC5IwoAAKA4sHtkZ+/evWrbtq18fX2VlJSkQYMGyc/PTytXrtSJEyf0wQcfOKNOAEAxdyd+Bxnf51Uy2D2yExkZqQEDBujIkSMqU6aMtb1Tp07asWOHQ4sDAAC4XXaHne+//17PP/98nva77777ht9FBQAA4Cp2hx1PT898vyX88OHDqly5skOKAgAAcBS7w07Xrl01efJkZWZmSpIsFotOnDihMWPGqEePHg4vEAAA4HbYHXZmz56t9PR0+fv76+rVq2rVqpVq1aql8uXL67XXXnNGjQAAAIVm991YPj4+2rhxo7755hv99NNPSk9P1wMPPKB27do5oz4AAIDbYnfYydW8eXM1b97ckbUAAAA4nN2XsYYNG6Z58+blaX/rrbc0fPhwR9QEAADgMHaHnc8++yzfEZ1HHnlEn376qUOKAgAAcBS7w8758+fl4+OTp93b21vnzp1zSFEAAACOYnfYqVWrltatW5enfe3atapZs6ZDigIAAHAUuycoR0ZGasiQIfr999/12GOPSZI2b96s2bNna+7cuY6uDwAA4LbYHXaeffZZZWRk6LXXXtOUKVMkSdWrV9eiRYvUt29fhxcIAABwOwp16/mLL76oF198Ub///ru8vLxUrlw5R9cFAADgEIV+zo4kvgsLAAAUe3ZPUD5z5oz69OmjoKAglSpVSu7u7jYvAACA4sTukZ3+/fvrxIkTmjBhgqpUqSKLxeKMugAAABzC7rCzc+dO/fe//1WTJk2cUA4AAIBj2X0ZKzg4WIZhOKMWAAAAh7M77MydO1djx45VUlKSE8oBAABwLLsvY/Xq1UtXrlxRSEiIypYtq9KlS9ssT0lJcVhxAAAAt8vusMNTkgEAwJ3E7rDTr18/Z9QBAADgFHbP2ZGkY8eOafz48Xrqqad09uxZSX98Eej+/fsdWhwAAMDtsjvsbN++XY0aNdK3336rlStXKj09XZL0008/KTo62uEFAgAA3A67w87YsWM1depUbdy4UR4eHtb2xx57TLt373ZocQAAALfL7rCzb98+/f3vf8/T7u/vr3PnzjmkKAAAAEexO+z4+vrq9OnTedr37Nmju+++2yFFAQAAOIrdYSc8PFxjxoxRcnKyLBaLcnJy9M0332jUqFHq27evM2oEAAAoNLvDzrRp01SvXj0FBwcrPT1dDRo0UMuWLfXII49o/PjxzqgRAACg0Ox6zo5hGEpOTta8efM0ceJE7du3T+np6br//vtVu3ZtZ9UIAABQaHaHnVq1amn//v2qXbu2goODnVUXAACAQ9h1GcvNzU21a9fW+fPnnVUPAACAQ9k9Z2fGjBkaPXq0fv75Z2fUAwAA4FB2fzdW3759deXKFTVu3FgeHh7y8vKyWc63ngMAgOKEbz0HAACmZlfYyczM1Pbt2zVhwgTVqFHDWTUBAAA4jF1hp3Tp0vrss880YcIEZ9UDoJirPvZrV5cAAHaxe4Jy9+7d9fnnnzuhFAAAAMeze85O7dq1NXnyZH3zzTdq2rSp7rrrLpvlw4YNc1hxAAAAt8vusLN48WL5+voqPj5e8fHxNsssFgthBwAAFCt2h53ExERn1AEAAOAUds/ZAQAAuJPYPbLz7LPP3nT5e++9V+hiAAAAHM3usHPhwgWb95mZmfr555918eJFPfbYYw4rDAAAwBHsDjurVq3K05aTk6MXX3xRISEhDikKAADAURwyZ8fNzU2RkZGaM2eOIzYHAADgMA6boHzs2DFlZWU5anMAAAAOYfdlrMjISJv3hmHo9OnT+vrrr9WvXz+HFQYAAOAIdoedPXv22Lx3c3NT5cqVNXv27FveqQUAAFDU7A47W7dudUYdAAAATmH3nJ3ExEQdOXIkT/uRI0eUlJTkiJoAAAAcxu6w079/f+3atStP+7fffqv+/fvbta0dO3aoS5cuCgoKksViyfNt6v3795fFYrF5dejQwaZPSkqKevfuLW9vb/n6+mrgwIFKT0+397AAAIBJ2R129uzZo+bNm+dpf/jhh5WQkGDXti5fvqzGjRtrwYIFN+zToUMHnT592vr65JNPbJb37t1b+/fv18aNG7V69Wrt2LFDgwcPtqsOAABgXnbP2bFYLLp06VKe9tTUVGVnZ9u1rY4dO6pjx4437ePp6anAwMB8lx08eFDr1q3T999/rwcffFCSNH/+fHXq1EmzZs1SUFCQXfUAAADzsXtkp2XLlpo+fbpNsMnOztb06dPVokULhxYnSdu2bZO/v7/q1q2rF198UefPn7cui4uLk6+vrzXoSFK7du3k5uamb7/99obbzMjIUFpams0LAACYk90jO6+//rpatmypunXr6tFHH5Uk/fe//1VaWpq2bNni0OI6dOigJ598UjVq1NCxY8f06quvqmPHjoqLi5O7u7uSk5Pl7+9vs06pUqXk5+en5OTkG253+vTpiomJcWitAACgeLJ7ZKdBgwbau3evevbsqbNnz+rSpUvq27evfvnlFzVs2NChxYWHh6tr165q1KiRunfvrtWrV+v777/Xtm3bbmu7UVFRSk1Ntb5OnjzpmIIBAECxY/fIjiQFBQVp2rRpjq7llmrWrKlKlSrp6NGjatu2rQIDA3X27FmbPllZWUpJSbnhPB/pj3lAnp6ezi4XAAAUA3aP7CxZskQrVqzI075ixQq9//77DinqRv73v//p/PnzqlKliiQpNDRUFy9eVHx8vLXPli1blJOTo2bNmjm1FgAAcGewO+xMnz5dlSpVytPu7+9v92hPenq6EhISrLesJyYmKiEhQSdOnFB6erpGjx6t3bt3KykpSZs3b1a3bt1Uq1YthYWFSZLq16+vDh06aNCgQfruu+/0zTffaMiQIQoPD+dOLAAAIKkQYefEiROqUaNGnvZq1arpxIkTdm3rhx9+0P3336/7779f0h9fMnr//fdr4sSJcnd31969e9W1a1fVqVNHAwcOVNOmTfXf//7X5hLUxx9/rHr16qlt27bq1KmTWrRooXfeecfewwIAACZl95wdf39/7d27V9WrV7dp/+mnn1SxYkW7ttW6dWsZhnHD5evXr7/lNvz8/LR06VK79gsAAEoOu0d2nnrqKQ0bNkxbt25Vdna2srOztWXLFr388ssKDw93Ro0AAACFZvfIzpQpU5SUlKS2bduqVKk/Vs/JyVHfvn1dcocWAADAzdgddjw8PLR8+XJNmTJFP/30k7y8vNSoUSNVq1bNGfUBAADclkI9Z0f6Y65MmzZt8r0zCwAAoLiwa87OxYsXFRERoUqVKikgIEABAQGqVKmShgwZoosXLzqpRAAAgMIr8MhOSkqKQkND9dtvv6l3796qX7++JOnAgQOKjY3V5s2btWvXLlWoUMFpxQIAANirwGFn8uTJ8vDw0LFjxxQQEJBnWfv27TV58mTNmTPH4UUCAAAUVoEvY33++eeaNWtWnqAjSYGBgZo5c6ZWrVrl0OIAAABuV4HDzunTp3XvvffecHnDhg2VnJzskKIAAAAcpcBhp1KlSkpKSrrh8sTERPn5+TmiJgAAAIcpcNgJCwvTuHHjdP369TzLMjIyNGHCBHXo0MGhxQEAANwuuyYoP/jgg6pdu7YiIiJUr149GYahgwcPauHChcrIyNCHH37ozFoBAADsVuCwU7VqVcXFxemll15SVFSU9Qs8LRaLHn/8cb311lsKDg52WqEAAACFYdcTlGvUqKG1a9fqwoULOnLkiCSpVq1azNUBAADFVqG+LqJChQp66KGHHF0LAACAw9n1dREAAAB3GsIOAAAwNcIOAAAwtQKFnQceeEAXLlyQ9Mct6FeuXHFqUQAAAI5SoLBz8OBBXb58WZIUExOj9PR0pxYFAADgKAW6G6tJkyYaMGCAWrRoIcMwNGvWLJUrVy7fvhMnTnRogQAAALejQGEnNjZW0dHRWr16tSwWi9auXatSpfKuarFYCDsAAKBYKVDYqVu3rpYtWyZJcnNz0+bNm+Xv7+/UwgAAABzB7ocK5uTkOKMOAAAApyjUE5SPHTumuXPn6uDBg5KkBg0a6OWXX1ZISIhDiwMAALhddj9nZ/369WrQoIG+++473Xfffbrvvvv07bff6t5779XGjRudUSMAAECh2T2yM3bsWI0YMUIzZszI0z5mzBg9/vjjDisOAADgdtk9snPw4EENHDgwT/uzzz6rAwcOOKQoAAAAR7E77FSuXFkJCQl52hMSErhDCwAAFDt2X8YaNGiQBg8erOPHj+uRRx6RJH3zzTd6/fXXFRkZ6fACAQAAbofdYWfChAkqX768Zs+eraioKElSUFCQJk2apGHDhjm8QAAAgNthd9ixWCwaMWKERowYoUuXLkmSypcv7/DCAAAAHKFQz9nJRcgBAADFnd0TlAEAAO4khB0AAGBqhB0AAGBqdoWdzMxMtW3bVkeOHHFWPQAAAA5lV9gpXbq09u7d66xaAAAAHM7uy1jPPPOMFi9e7IxaAAAAHM7uW8+zsrL03nvvadOmTWratKnuuusum+Vvvvmmw4oDAAC4XXaHnZ9//lkPPPCAJOnw4cM2yywWi2OqAgAAcBC7w87WrVudUQcAAIBTFPrW86NHj2r9+vW6evWqJMkwDIcVBQAA4Ch2h53z58+rbdu2qlOnjjp16qTTp09LkgYOHKiRI0c6vEAAAIDbYXfYGTFihEqXLq0TJ06obNmy1vZevXpp3bp1Di0OAADgdtk9Z2fDhg1av369qlatatNeu3Zt/frrrw4rDAAAwBHsHtm5fPmyzYhOrpSUFHl6ejqkKAAAAEexO+w8+uij+uCDD6zvLRaLcnJyNHPmTLVp08ahxQEAANwuuy9jzZw5U23bttUPP/yg69ev65VXXtH+/fuVkpKib775xhk1AgAAFJrdIzsNGzbU4cOH1aJFC3Xr1k2XL1/Wk08+qT179igkJMQZNQIAABSa3SM7kuTj46Nx48Y5uhYAAACHK1TYuXDhghYvXqyDBw9Kkho0aKABAwbIz8/PocUBAADcLrsvY+3YsUPVq1fXvHnzdOHCBV24cEHz5s1TjRo1tGPHDmfUCAAAUGh2j+xERESoV69eWrRokdzd3SVJ2dnZeumllxQREaF9+/Y5vEgAAIDCsntk5+jRoxo5cqQ16EiSu7u7IiMjdfToUYcWBwAAcLvsDjsPPPCAda7Onx08eFCNGzd2SFEAAACOUqCws3fvXutr2LBhevnllzVr1izt3LlTO3fu1KxZszRixAiNGDHCrp3v2LFDXbp0UVBQkCwWiz7//HOb5YZhaOLEiapSpYq8vLzUrl07HTlyxKZPSkqKevfuLW9vb/n6+mrgwIFKT0+3qw4AAGBeBZqz06RJE1ksFhmGYW175ZVX8vR7+umn1atXrwLv/PLly2rcuLGeffZZPfnkk3mWz5w5U/PmzdP777+vGjVqaMKECQoLC9OBAwdUpkwZSVLv3r11+vRpbdy4UZmZmRowYIAGDx6spUuXFrgOAABgXgUKO4mJiU7ZeceOHdWxY8d8lxmGoblz52r8+PHq1q2bJOmDDz5QQECAPv/8c4WHh+vgwYNat26dvv/+ez344IOSpPnz56tTp06aNWuWgoKC8t12RkaGMjIyrO/T0tIcfGQAAKC4KFDYqVatmrPryCMxMVHJyclq166dtc3Hx0fNmjVTXFycwsPDFRcXJ19fX2vQkaR27drJzc1N3377rf7+97/nu+3p06crJibG6ccAAABcr1APFTx16pR27typs2fPKicnx2bZsGHDHFJYcnKyJCkgIMCmPSAgwLosOTlZ/v7+NstLlSolPz8/a5/8REVFKTIy0vo+LS1NwcHBDqkbAAAUL3aHndjYWD3//PPy8PBQxYoVZbFYrMssFovDwo4zeXp6ytPT09VlAACAImB32JkwYYImTpyoqKgoubnZfed6gQUGBkqSzpw5oypVqljbz5w5oyZNmlj7nD171ma9rKwspaSkWNcHAAAlm91p5cqVKwoPD3dq0JGkGjVqKDAwUJs3b7a2paWl6dtvv1VoaKgkKTQ0VBcvXlR8fLy1z5YtW5STk6NmzZo5tT4AAHBnsDuxDBw4UCtWrHDIztPT05WQkKCEhARJf0xKTkhI0IkTJ2SxWDR8+HBNnTpVX375pfbt26e+ffsqKChI3bt3lyTVr19fHTp00KBBg/Tdd9/pm2++0ZAhQxQeHn7DO7EAAEDJYjH+/PCcAsjOztYTTzyhq1evqlGjRipdurTN8jfffLPA29q2bZvatGmTp71fv36KjY2VYRiKjo7WO++8o4sXL6pFixZauHCh6tSpY+2bkpKiIUOG6KuvvpKbm5t69OihefPmqVy5cgWuIy0tTT4+PkpNTZW3t3eB1wNKoupjv3Z1CYDDJM3o7OoScBsK+vvb7rAzdepUTZw4UXXr1lVAQECeCcpbtmwpfNUuQtgBCo6wAzMh7NzZCvr72+4JyrNnz9Z7772n/v373059AAAARcLuOTuenp5q3ry5M2oBAABwOLvDzssvv6z58+c7oxYAAACHs/sy1nfffactW7Zo9erVuvfee/NMUF65cqXDigMAALhddocdX1/ffL+hHAAAoDiyO+wsWbLEGXUAAAA4hXMfgwwAAOBido/s1KhRw+bZOn91/Pjx2yoIAADAkewOO8OHD7d5n5mZqT179mjdunUaPXq0o+oCAABwCLvDzssvv5xv+4IFC/TDDz/cdkEAAACO5LA5Ox07dtRnn33mqM0BAAA4hMPCzqeffio/Pz9HbQ4AAMAh7L6Mdf/999tMUDYMQ8nJyfr999+1cOFChxYHAABwu+wOO927d7d57+bmpsqVK6t169aqV6+eo+oCAABwCLvDTnR0tDPqAAAAcAoeKggAAEytwCM7bm5uN32YoCRZLBZlZWXddlEAAACOUuCws2rVqhsui4uL07x585STk+OQogAAABylwGGnW7duedoOHTqksWPH6quvvlLv3r01efJkhxYHAABwuwo1Z+fUqVMaNGiQGjVqpKysLCUkJOj9999XtWrVHF0fAADAbbEr7KSmpmrMmDGqVauW9u/fr82bN+urr75Sw4YNnVUfAADAbSnwZayZM2fq9ddfV2BgoD755JN8L2sBAAAUNxbDMIyCdHRzc5OXl5fatWsnd3f3G/ZbuXKlw4orKmlpafLx8VFqaqq8vb1dXQ5QrFUf+7WrSwAcJmlGZ1eXgNtQ0N/fBR7Z6du37y1vPQcAAChuChx2YmNjnVgGAACAc/AEZQAAYGqEHQAAYGqEHQAAYGqEHQAAYGoFnqAMwLG4hRsAigYjOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNR4gjIAoMS6E59knjSjs6tLuOMwsgMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEytWIedSZMmyWKx2Lzq1atnXX7t2jVFRESoYsWKKleunHr06KEzZ864sGIAAFDcFOuwI0n33nuvTp8+bX3t3LnTumzEiBH66quvtGLFCm3fvl2nTp3Sk08+6cJqAQBAcVPK1QXcSqlSpRQYGJinPTU1VYsXL9bSpUv12GOPSZKWLFmi+vXra/fu3Xr44YdvuM2MjAxlZGRY36elpTm+cAAAUCwU+5GdI0eOKCgoSDVr1lTv3r114sQJSVJ8fLwyMzPVrl07a9969erpnnvuUVxc3E23OX36dPn4+FhfwcHBTj0GAADgOsU67DRr1kyxsbFat26dFi1apMTERD366KO6dOmSkpOT5eHhIV9fX5t1AgIClJycfNPtRkVFKTU11fo6efKkE48CAAC4UrG+jNWxY0frz/fdd5+aNWumatWq6T//+Y+8vLwKvV1PT095eno6okQAAFDMFeuRnb/y9fVVnTp1dPToUQUGBur69eu6ePGiTZ8zZ87kO8cHAACUTHdU2ElPT9exY8dUpUoVNW3aVKVLl9bmzZutyw8dOqQTJ04oNDTUhVUCAIDipFhfxho1apS6dOmiatWq6dSpU4qOjpa7u7ueeuop+fj4aODAgYqMjJSfn5+8vb01dOhQhYaG3vROLAAAULIU67Dzv//9T0899ZTOnz+vypUrq0WLFtq9e7cqV64sSZozZ47c3NzUo0cPZWRkKCwsTAsXLnRx1QAAoDixGIZhuLoIV0tLS5OPj49SU1Pl7e3t6nJQQlQf+7WrSwBwB0qa0dnVJRQbBf39fUfN2QEAALAXYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJhaKVcXADhC9bFfu7oEAEAxxcgOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNZ6zAwDAHeROfK5Y0ozOLt0/IzsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDU+LoI5HEnPoocAIAbYWQHAACYmmnCzoIFC1S9enWVKVNGzZo103fffefqkgAAQDFgirCzfPlyRUZGKjo6Wj/++KMaN26ssLAwnT171tWlAQAAFzNF2HnzzTc1aNAgDRgwQA0aNNDbb7+tsmXL6r333nN1aQAAwMXu+AnK169fV3x8vKKioqxtbm5uateuneLi4vJdJyMjQxkZGdb3qampkqS0tDSH19cwer3DtwkAwJ3EGb9f/7xdwzBu2u+ODzvnzp1Tdna2AgICbNoDAgL0yy+/5LvO9OnTFRMTk6c9ODjYKTUCAFCS+cx17vYvXbokHx+fGy6/48NOYURFRSkyMtL6PicnRykpKapYsaIsFosLK3OetLQ0BQcH6+TJk/L29nZ1Ofj/cF6KL85N8cW5KZ5ccV4Mw9ClS5cUFBR00353fNipVKmS3N3ddebMGZv2M2fOKDAwMN91PD095enpadPm6+vrrBKLFW9vb/7nUAxxXoovzk3xxbkpnor6vNxsRCfXHT9B2cPDQ02bNtXmzZutbTk5Odq8ebNCQ0NdWBkAACgO7viRHUmKjIxUv3799OCDD+qhhx7S3LlzdfnyZQ0YMMDVpQEAABczRdjp1auXfv/9d02cOFHJyclq0qSJ1q1bl2fScknm6emp6OjoPJfv4Fqcl+KLc1N8cW6Kp+J8XizGre7XAgAAuIPd8XN2AAAAboawAwAATI2wAwAATI2wAwAATI2wAwAATI2wYyILFixQ9erVVaZMGTVr1kzfffddgdZbtmyZLBaLunfv7twCSyh7z8vFixcVERGhKlWqyNPTU3Xq1NGaNWuKqNqSxd5zM3fuXNWtW1deXl4KDg7WiBEjdO3atSKqtmTYsWOHunTpoqCgIFksFn3++ee3XGfbtm164IEH5OnpqVq1aik2NtbpdZZE9p6blStX6vHHH1flypXl7e2t0NBQrV/vmi/HJuyYxPLlyxUZGano6Gj9+OOPaty4scLCwnT27NmbrpeUlKRRo0bp0UcfLaJKSxZ7z8v169f1+OOPKykpSZ9++qkOHTqkf//737r77ruLuHLzs/fcLF26VGPHjlV0dLQOHjyoxYsXa/ny5Xr11VeLuHJzu3z5sho3bqwFCxYUqH9iYqI6d+6sNm3aKCEhQcOHD9dzzz3nsl+qZmbvudmxY4cef/xxrVmzRvHx8WrTpo26dOmiPXv2OLnSfBgwhYceesiIiIiwvs/OzjaCgoKM6dOn33CdrKws45FHHjHeffddo1+/fka3bt2KoNKSxd7zsmjRIqNmzZrG9evXi6rEEsvecxMREWE89thjNm2RkZFG8+bNnVpnSSbJWLVq1U37vPLKK8a9995r09arVy8jLCzMiZWhIOcmPw0aNDBiYmIcX9AtMLJjAtevX1d8fLzatWtnbXNzc1O7du0UFxd3w/UmT54sf39/DRw4sCjKLHEKc16+/PJLhYaGKiIiQgEBAWrYsKGmTZum7Ozsoiq7RCjMuXnkkUcUHx9vvdR1/PhxrVmzRp06dSqSmpG/uLg4m/MoSWFhYTf9fx9cIycnR5cuXZKfn1+R79sUXxdR0p07d07Z2dl5vh4jICBAv/zyS77r7Ny5U4sXL1ZCQkIRVFgyFea8HD9+XFu2bFHv3r21Zs0aHT16VC+99JIyMzMVHR1dFGWXCIU5N08//bTOnTunFi1ayDAMZWVl6YUXXuAyloslJyfnex7T0tJ09epVeXl5uagy/NWsWbOUnp6unj17Fvm+GdkpgS5duqQ+ffro3//+typVquTqcvAnOTk58vf31zvvvKOmTZuqV69eGjdunN5++21Xl1bibdu2TdOmTdPChQv1448/auXKlfr66681ZcoUV5cGFHtLly5VTEyM/vOf/8jf37/I98/IjglUqlRJ7u7uOnPmjE37mTNnFBgYmKf/sWPHlJSUpC5duljbcnJyJEmlSpXSoUOHFBIS4tyiSwB7z4skValSRaVLl5a7u7u1rX79+kpOTtb169fl4eHh1JpLisKcmwkTJqhPnz567rnnJEmNGjXS5cuXNXjwYI0bN05ubvzb0RUCAwPzPY/e3t6M6hQTy5Yt03PPPacVK1bkueRYVPjbaQIeHh5q2rSpNm/ebG3LycnR5s2bFRoamqd/vXr1tG/fPiUkJFhfXbt2td7NEBwcXJTlm5a950WSmjdvrqNHj1rDpyQdPnxYVapUIeg4UGHOzZUrV/IEmtxQavB9yi4TGhpqcx4laePGjTc8jyhan3zyiQYMGKBPPvlEnTt3dl0hRT4lGk6xbNkyw9PT04iNjTUOHDhgDB482PD19TWSk5MNwzCMPn36GGPHjr3h+tyN5Rz2npcTJ04Y5cuXN4YMGWIcOnTIWL16teHv729MnTrVVYdgWvaem+joaKN8+fLGJ598Yhw/ftzYsGGDERISYvTs2dNVh2BKly5dMvbs2WPs2bPHkGS8+eabxp49e4xff/3VMAzDGDt2rNGnTx9r/+PHjxtly5Y1Ro8ebRw8eNBYsGCB4e7ubqxbt85Vh2Ba9p6bjz/+2ChVqpSxYMEC4/Tp09bXxYsXi7x2wo6JzJ8/37jnnnsMDw8P46GHHjJ2795tXdaqVSujX79+N1yXsOM89p6XXbt2Gc2aNTM8PT2NmjVrGq+99pqRlZVVxFWXDPacm8zMTGPSpElGSEiIUaZMGSM4ONh46aWXjAsXLhR94Sa2detWQ1KeV+656Nevn9GqVas86zRp0sTw8PAwatasaSxZsqTI6y4J7D03rVq1umn/omQxDMZfAQCAeTFnBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmNr/Az8YisMOXLK5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dispersion.plots.all()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, one may also export the ellipses to a ``.kml`` file so it can be easily visualized in Google Earth\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "test_dispersion.export_ellipses_to_kml(\n",
    "    filename=\"monte_carlo_analysis_outputs/monte_carlo_class_example.kml\",\n",
    "    origin_lat=env.latitude,\n",
    "    origin_lon=env.longitude,\n",
    "    type=\"impact\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom exports using callback functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have shown, so far, how to perform to use the `MonteCarlo` class and visualize its results. By default, some variables exported to the output files, such as *apogee* and *x_impact*. The `export_list` argument provides a simplified way for the user to export additional variables listed in the documentation, such as *inclination* and *heading*.  \n",
    "\n",
    "There are applications in which you might need to extract more information in the results than the `export_list` argument can handle. To that end, the `MonteCarlo` class has a `data_collector` argument which allows you customize further the output of the simulation.\n",
    "\n",
    "To exemplify its use, we show how to export the *date* of the environment used in the simulation together with the *average reynolds number* along with the default variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the `stochastic_env`, `stochastic_rocket` and `stochastic_flight` objects previously defined, and only change the `MonteCarlo` object. First, we need to define our customized data collector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "# Defining custom callback functions\n",
    "def get_average_reynolds_number(flight):\n",
    "    reynold_number_list = flight.reynolds_number(flight.time)\n",
    "    average_reynolds_number = np.mean(reynold_number_list)\n",
    "    return average_reynolds_number\n",
    "\n",
    "\n",
    "def get_date(flight):\n",
    "    return flight.env.date\n",
    "\n",
    "\n",
    "custom_data_collector = {\n",
    "    \"average_reynolds_number\": get_average_reynolds_number,\n",
    "    \"date\": get_date,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `data_collector` must be a dictionary whose keys are the names of the variables we want to export and the values are callback functions (python callables) that compute these variable values. Notice how we can compute complex expressions in this function and just export the result. For instance, the *get_average_reynolds_number* calls the `flight.reynolds_number` method for each value in `flight.time` list and computes the average value using numpy's `mean`. The *date* variable is straightforward.\n",
    "\n",
    "After we define the data collector, we pass it as an argument to the `MonteCarlo` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dispersion = MonteCarlo(\n",
    "    filename=\"monte_carlo_analysis_outputs/monte_carlo_class_example_customized\",\n",
    "    environment=stochastic_env,\n",
    "    rocket=stochastic_rocket,\n",
    "    flight=stochastic_flight,\n",
    "    export_list=[\"apogee\", \"apogee_time\", \"x_impact\"],\n",
    "    data_collector=custom_data_collector,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dispersion.simulate(number_of_simulations=10, append=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dispersion.prints.all()"
   ]
  }
 ],
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